493
Views
179
CrossRef citations to date
0
Altmetric
Original Articles

STATLOG: COMPARISON OF CLASSIFICATION ALGORITHMS ON LARGE REAL-WORLD PROBLEMS

, &
Pages 289-333 | Published online: 15 May 2007

Keep up to date with the latest research on this topic with citation updates for this article.

Read on this site (4)

Zhao Lu, Haoda Fu & Gangbing Song. (2015) A multi-scale non-parametric and parametric hybrid multi-category recognition algorithm with probabilistic outputs. Journal of Experimental & Theoretical Artificial Intelligence 27:4, pages 487-500.
Read now
Dominik Heider, Sascha Hauke, Martin Pyka & Daniel Kessler. (2010) Insights into the classification of small GTPases. Advances and Applications in Bioinformatics and Chemistry 3, pages 15-24.
Read now
Zhao Lu, Feng Lin & Hao Ying. (2007) DESIGN OF DECISION TREE VIA KERNELIZED HIERARCHICAL CLUSTERING FOR MULTICLASS SUPPORT VECTOR MACHINES. Cybernetics and Systems 38:2, pages 187-202.
Read now
LUIS M. DE CAMPOS. (1998) Independency relationships and learning algorithms for singly connected networks. Journal of Experimental & Theoretical Artificial Intelligence 10:4, pages 511-549.
Read now

Articles from other publishers (175)

Sheik Murad Hassan Anik, Xinghua Gao & Na Meng. (2023) Towards automated occupant profile creation in smart buildings: A machine learning-enabled approach for user persona generation. Energy and Buildings 297, pages 113485.
Crossref
Mohamed Elhefnawy, Mohamed-Salah Ouali, Ahmed Ragab & Mouloud Amazouz. (2023) Fusion of heterogeneous industrial data using polygon generation & deep learning. Results in Engineering 19, pages 101234.
Crossref
Xianghua Chu, Shuxiang Li, Fei Gao, Can Cui, Forest Pfeiffer & Jianshuang Cui. (2023) A data-driven meta-learning recommendation model for multi-mode resource constrained project scheduling problem. Computers & Operations Research 157, pages 106290.
Crossref
David Breitenmoser, Horst-Michael Prasser, Annalisa Manera & Victor Petrov. (2023) Machine learning based flow regime recognition in helically coiled tubes using X-ray radiography. International Journal of Multiphase Flow 161, pages 104382.
Crossref
Marc Gürtler & Marvin Zöllner. (2022) Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method. OR Spectrum 45:1, pages 251-287.
Crossref
Kei Hirose, Kanta Miura & Atori Koie. (2023) Hierarchical clustered multiclass discriminant analysis via cross-validation. Computational Statistics & Data Analysis 178, pages 107613.
Crossref
Marcos MonteiroJrJr, Alceu S. BrittoJrJr, Jean P. Barddal, Luiz S. Oliveira & Robert Sabourin. (2023) Exploring diversity in data complexity and classifier decision spaces for pool generation. Information Fusion 89, pages 567-587.
Crossref
Do Thu Ha, Ta Phuong Bac, Kim Duc Tran & Kim Phuc Tran. 2023. Artificial Intelligence for Smart Manufacturing. Artificial Intelligence for Smart Manufacturing 145 166 .
Mehmet Şata & Fuat ELKONCA. (2022) A Comparison of Classification Performances between the Methods of Logistics Regression and CHAID Analysis in accordance with Sample Size. International Journal of Contemporary Educational Research 7:2, pages 15-26.
Crossref
Mariel F. Musso, Lina M. Cómbita, Eduardo C. Cascallar & M. Rosario Rueda. (2022) Modeling the Contribution of Genetic Variation to Cognitive Gains Following Training with a Machine Learning Approach. Mind, Brain, and Education.
Crossref
Ederson Schmeing, André Luiz Brun & Ronan Assumpção Silva. (2022) Dynamic selection of classifiers based on complexity measures. Dynamic selection of classifiers based on complexity measures.
Qamar Alfalouji, Piergiorgio Sartor & Pietro Zanuttigh. (2022) Reframing control methods for parameters optimization in adversarial image generation. Neural Networks 153, pages 303-313.
Crossref
Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski & Dragi Kocev. (2022) Explaining the performance of multilabel classification methods with data set properties. International Journal of Intelligent Systems 37:9, pages 6080-6122.
Crossref
Zahra Khalilzad, Yasmina Kheddache & Chakib Tadj. (2022) An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems. Entropy 24:9, pages 1194.
Crossref
Samar Ali Shilbayeh & Sunil Vadera. (2021) Cost-sensitive meta-learning framework. Journal of Modelling in Management 17:3, pages 987-1007.
Crossref
Mohamed Elhefnawy, Ahmed Ragab & Mohamed-Salah Ouali. (2021) Fault classification in the process industry using polygon generation and deep learning. Journal of Intelligent Manufacturing 33:5, pages 1531-1544.
Crossref
Ishwar Baidari & Nagaraj Honnikoll. (2021) Bhattacharyya distance based concept drift detection method for evolving data stream. Expert Systems with Applications 183, pages 115303.
Crossref
José Pedro Monteiro, Diogo Ramos, Davide Carneiro, Francisco Duarte, João M. Fernandes & Paulo Novais. (2021) Meta‐learning and the new challenges of machine learning. International Journal of Intelligent Systems 36:11, pages 6240-6272.
Crossref
Juuso Eronen, Michal Ptaszynski, Fumito Masui, Aleksander Smywiński-Pohl, Gniewosz Leliwa & Michal Wroczynski. (2021) Improving classifier training efficiency for automatic cyberbullying detection with Feature Density. Information Processing & Management 58:5, pages 102616.
Crossref
Pengfei Ma, Zunqian Zhang, Jiahao Wang, Wei Zhang, Jiajia Liu, Qiyuan Lu & Ziqi Wang. (2021) Review on the Application of Metalearning in Artificial Intelligence. Computational Intelligence and Neuroscience 2021, pages 1-12.
Crossref
Roberto Porto, José M. Molina, Antonio Berlanga & Miguel A. Patricio. (2021) Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set. Applied Sciences 11:3, pages 1285.
Crossref
Marcos Monteiro, Alceu S. Britto, Jean P. Barddal, Luiz S. Oliveira & Robert Sabourin. (2021) Classifier Pool Generation based on a Two-level Diversity Approach. Classifier Pool Generation based on a Two-level Diversity Approach.
Pawel Powroznik, Piotr Wojcicki & Slawomir W. Przylucki. (2021) Scalogram as a Representation of Emotional Speech. IEEE Access 9, pages 154044-154057.
Crossref
Robercy Alves Da Silva, Anne Magaly De Paula Canuto, Cephas Alves Da Silveira Barreto & Joao Carlos Xavier-Junior. (2021) Automatic Recommendation Method for Classifier Ensemble Structure Using Meta-Learning. IEEE Access 9, pages 106254-106268.
Crossref
Alireza Ghods & Diane J. Cook. (2020) A survey of deep network techniques all classifiers can adopt. Data Mining and Knowledge Discovery 35:1, pages 46-87.
Crossref
Katarzyna Woźnica & Przemysław Biecek. 2021. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. Machine Learning and Principles and Practice of Knowledge Discovery in Databases 505 520 .
Ishwar Baidari & Nagaraj Honnikoll. (2020) Accuracy weighted diversity-based online boosting. Expert Systems with Applications 160, pages 113723.
Crossref
Evangelos Alevizos. (2020) A Combined Machine Learning and Residual Analysis Approach for Improved Retrieval of Shallow Bathymetry from Hyperspectral Imagery and Sparse Ground Truth Data. Remote Sensing 12:21, pages 3489.
Crossref
Mariel F. Musso, Eduardo C. Cascallar, Neda Bostani & Michael Crawford. (2020) Identifying Reliable Predictors of Educational Outcomes Through Machine-Learning Predictive Modeling. Frontiers in Education 5.
Crossref
Ronan Assumpção Silva, Alceu S. BrittoJr.Jr., Fabricio Enembreck, Robert Sabourin & Luiz S. Oliveira. (2020) Selecting and Combining Classifiers Based on Centrality Measures. International Journal on Artificial Intelligence Tools 29:03n04, pages 2060004.
Crossref
Ronan Assumpção Silva, Alceu de Souza Britto Jr, Fabricio Enembreck, Robert Sabourin & Luiz E. S. de Oliveira. (2019) CSBF: A static ensemble fusion method based on the centrality score of complex networks. Computational Intelligence 36:2, pages 522-556.
Crossref
Peter Mills. (2018) Accelerating kernel classifiers through borders mapping. Journal of Real-Time Image Processing 17:2, pages 313-327.
Crossref
Sofie De Cnudde, David Martens, Theodoros Evgeniou & Foster Provost. (2019) A benchmarking study of classification techniques for behavioral data. International Journal of Data Science and Analytics 9:2, pages 131-173.
Crossref
Rakan A. Alsowail & Taher Al-Shehari. (2020) Empirical Detection Techniques of Insider Threat Incidents. IEEE Access 8, pages 78385-78402.
Crossref
Roberto Porto Solano & Jose M. Molina. 2020. Hybrid Artificial Intelligent Systems. Hybrid Artificial Intelligent Systems 612 619 .
Arnoldo UberJr.Jr., Ricardo Azambuja Silveira, Paulo Jose de Freitas Filho, Julio Cezar Uzinski & Reinaldo Augusto da Costa Bianchi. 2020. Advances in Computational Intelligence. Advances in Computational Intelligence 419 434 .
Yonatan-Carlos Carranza-Alarcon, Soundouss Messoudi & Sébastien Destercke. 2020. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Information Processing and Management of Uncertainty in Knowledge-Based Systems 96 111 .
Mohamed Nasor & Sharaz Ali. (2019) Performance Analysis and Ranking of Data Mining Algorithms Across Multiple Datasets. Performance Analysis and Ranking of Data Mining Algorithms Across Multiple Datasets.
SenPeng Chen, Jia Wu & XiuYun Chen. (2019) Deep Reinforcement Learning with Model-Based Acceleration for Hyperparameter Optimization. Deep Reinforcement Learning with Model-Based Acceleration for Hyperparameter Optimization.
Peter Mills. (2019) Solving for multi-class using orthogonal coding matrices. SN Applied Sciences 1:11.
Crossref
Xiaoyun Sun, Shijie Gong, Guang Han, Mingming Wang & An Jin. (2018) Pruning Elman neural network and its application in bolt defects classification. International Journal of Machine Learning and Cybernetics 10:7, pages 1847-1862.
Crossref
Katarina Mitrovic, Danijela Milosevic & Marian Greconici. (2019) Comparison of Machine Learning Algorithms for Shelter Animal Classification. Comparison of Machine Learning Algorithms for Shelter Animal Classification.
Asim Roy, Shiban QureshiKartikeya PandeDivitha NairKartik GairolaPooja JainSuraj SinghKirti SharmaAkshay JagadaleYi-Yang LinShashank SharmaRamya GotetyYuexin ZhangJi Tang, Tejas MehtaHemanth SindhanuruNonso OkaforSantak DasChidambara N. GopalSrinivasa B. RudrarajuAvinash V. Kakarlapudi. (2019) Performance Comparison of Machine Learning Platforms. INFORMS Journal on Computing 31:2, pages 207-225.
Crossref
P. Baumann, D.S. Hochbaum & Y.T. Yang. (2019) A comparative study of the leading machine learning techniques and two new optimization algorithms. European Journal of Operational Research 272:3, pages 1041-1057.
Crossref
Shahabeddin Abhari, Sharareh R. Niakan Kalhori, Mehdi Ebrahimi, Hajar Hasannejadasl & Ali Garavand. (2019) Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods. Healthcare Informatics Research 25:4, pages 248.
Crossref
Thee Chanyaswad, Changchang Liu & Prateek Mittal. (2019) RON-Gauss: Enhancing Utility in Non-Interactive Private Data Release. Proceedings on Privacy Enhancing Technologies 2019:1, pages 26-46.
Crossref
Mariana A. Souza, George D.C. Cavalcanti, Rafael M.O. Cruz & Robert Sabourin. (2019) Online local pool generation for dynamic classifier selection. Pattern Recognition 85, pages 132-148.
Crossref
Subodhini Gupta, Bhushan Parekh & Anjali Jivani. 2019. Advanced Informatics for Computing Research. Advanced Informatics for Computing Research 383 396 .
Josué Melka & Jean-Jacques Mariage. 2019. Computational Intelligence. Computational Intelligence 139 161 .
Matthias Feurer & Frank Hutter. 2019. Automated Machine Learning. Automated Machine Learning 3 33 .
Ronan Assumpcao Silva, Alceu S. Britto, Fabricio Enembreck, Robert Sabourin & Luis S. Oliveira. (2018) Fusion of Classifiers Based on Centrality Measures. Fusion of Classifiers Based on Centrality Measures.
Jose Augusto S. Lustosa Filho, Anne M.P. Canuto & Regivan Hugo Nunes Santiago. (2018) Investigating the impact of selection criteria in dynamic ensemble selection methods. Expert Systems with Applications 106, pages 141-153.
Crossref
Marcos Antonio Mouriño-García, Roberto Pérez-Rodríguez, Luis Anido-Rifón & Manuel Vilares-Ferro. (2018) Wikipedia-based hybrid document representation for textual news classification. Soft Computing 22:18, pages 6047-6065.
Crossref
A. Abayomi, O.O. Olugbara & D. Heukelman. (2018) An Architecture Utilizing Human Emotions and Activities Recognition for Remote Monitoring. An Architecture Utilizing Human Emotions and Activities Recognition for Remote Monitoring.
Rafael M.O. Cruz, Robert Sabourin & George D.C. Cavalcanti. (2018) Dynamic classifier selection: Recent advances and perspectives. Information Fusion 41, pages 195-216.
Crossref
Thee Chanyaswad, Mert Al & S. Y. Kung. (2018) Outlier Removal for Enhancing Kernel-Based Classifier Via the Discriminant Information. Outlier Removal for Enhancing Kernel-Based Classifier Via the Discriminant Information.
André L. Brun, Alceu S. BrittoJr.Jr., Luiz S. Oliveira, Fabricio Enembreck & Robert Sabourin. (2018) A framework for dynamic classifier selection oriented by the classification problem difficulty. Pattern Recognition 76, pages 175-190.
Crossref
Rafael M. O. Cruz, Robert Sabourin & George D. C. Cavalcanti. (2016) Prototype selection for dynamic classifier and ensemble selection. Neural Computing and Applications 29:2, pages 447-457.
Crossref
Manh Truong Dang, Anh Vu Luong, Tuyet-Trinh Vu, Quoc Viet Hung Nguyen, Tien Thanh Nguyen & Bela Stantic. 2018. Intelligent Information and Database Systems. Intelligent Information and Database Systems 576 586 .
Yiyan Zhang, Yi Xin, Qin Li, Jianshe Ma, Shuai Li, Xiaodan Lv & Weiqi Lv. (2017) Empirical study of seven data mining algorithms on different characteristics of datasets for biomedical classification applications. BioMedical Engineering OnLine 16:1.
Crossref
Tri Doan & Jugal Kalita. (2016) Predicting run time of classification algorithms using meta-learning. International Journal of Machine Learning and Cybernetics 8:6, pages 1929-1943.
Crossref
Amichai Painsky & Saharon Rosset. (2017) Cross-Validated Variable Selection in Tree-Based Methods Improves Predictive Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence 39:11, pages 2142-2153.
Crossref
Chongsheng Zhang, Changchang Liu, Xiangliang Zhang & George Almpanidis. (2017) An up-to-date comparison of state-of-the-art classification algorithms. Expert Systems with Applications 82, pages 128-150.
Crossref
Darren M. Chitty. (2016) Faster GPU-based genetic programming using a two-dimensional stack. Soft Computing 21:14, pages 3859-3878.
Crossref
Daniela Lagomarsino, V. Tofani, S. Segoni, F. Catani & N. Casagli. (2017) A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling. Environmental Modeling & Assessment 22:3, pages 201-214.
Crossref
Mariana A. Souza, George D. C. Cavalcanti, Rafael M. O. Cruz & Robert Sabourin. (2017) On the characterization of the Oracle for dynamic classifier selection. On the characterization of the Oracle for dynamic classifier selection.
Ya'nan Liang, Dongya Qin, Yonghong Zhang, Wanqian Liu & Guizhao Liang. (2017) Comprehensive Interactions of ACE Inhibitors With Their Receptor by a Support Vector Machine Model and Molecular Docking. Journal of the Chinese Chemical Society 64:3, pages 310-320.
Crossref
Ping Lu, Haitao Liu, Christopher Serratella & Xiaozhi Wang. (2017) Assessment of Data-Driven, Machine Learning Techniques for Machinery Prognostics of Offshore Assets. Assessment of Data-Driven, Machine Learning Techniques for Machinery Prognostics of Offshore Assets.
Eva Garcia-Martin, Niklas Lavesson & Håkan Grahn. 2017. Green, Pervasive, and Cloud Computing. Green, Pervasive, and Cloud Computing 267 281 .
Anandarup Roy, Rafael M.O. Cruz, Robert Sabourin & George D. C. Cavalcanti. (2016) Meta-regression based pool size prediction scheme for dynamic selection of classifiers. Meta-regression based pool size prediction scheme for dynamic selection of classifiers.
Anandarup Roy, Rafael M.O. Cruz, Robert Sabourin & George D.C. Cavalcanti. (2016) Meta-learning recommendation of default size of classifier pool for META-DES. Neurocomputing 216, pages 351-362.
Crossref
Jared Kibele & Nick T. Shears. (2016) Nonparametric Empirical Depth Regression for Bathymetric Mapping in Coastal Waters. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9:11, pages 5130-5138.
Crossref
Marcos Antonio Mourino Garcia, Roberto Perez Rodriguez, Manuel Vilares Ferro & Luis Anido Rifon. (2016) Wikipedia-Based Hybrid Document Representation for Textual News Classification. Wikipedia-Based Hybrid Document Representation for Textual News Classification.
Mohammed Elmahgiubi, Omar Ahmed, Shawki Areibi & Gary Grewal. (2016) Efficient algorithm selection for packet classification using machine learning. Efficient algorithm selection for packet classification using machine learning.
Andre L. Brun, Alceu S. Britto, Luiz S. Oliveira, Fabricio Enembreck & Robert Sabourin. (2016) Contribution of data complexity features on dynamic classifier selection. Contribution of data complexity features on dynamic classifier selection.
Xinghang Dai, Nada Matta & Guillaume Ducellier. 2016. Daily Knowledge Valuation in Organizations. Daily Knowledge Valuation in Organizations 73 94 .
Susan Lomax & Sunil Vadera. (2016) A Cost-Sensitive Decision Tree Learning Algorithm Based on a Multi-Armed Bandit Framework. The Computer Journal, pages bxw015.
Crossref
Marcelo R.P. Ferreira, Francisco de A.T. de Carvalho & Eduardo C. Simões. (2016) Kernel-based hard clustering methods with kernelization of the metric and automatic weighting of the variables. Pattern Recognition 51, pages 310-321.
Crossref
Darren M. Chitty. (2014) Improving the performance of GPU-based genetic programming through exploitation of on-chip memory. Soft Computing 20:2, pages 661-680.
Crossref
Yuping Li. 2016. Advanced Multimedia and Ubiquitous Engineering. Advanced Multimedia and Ubiquitous Engineering 127 132 .
William Raynaut, Chantal Soule-Dupuy & Nathalie Valles-Parlangeau. 2016. AI 2016: Advances in Artificial Intelligence. AI 2016: Advances in Artificial Intelligence 215 228 .
Geert Gins, Sam Wuyts, Sander Van den Zegel & Jan Van Impe. 2016. Advances in Data Mining. Applications and Theoretical Aspects. Advances in Data Mining. Applications and Theoretical Aspects 334 348 .
Guangtao Wang, Qinbao Song & Xiaoyan Zhu. (2015) An improved data characterization method and its application in classification algorithm recommendation. Applied Intelligence 43:4, pages 892-912.
Crossref
Tri Doan & Jugal Kalita. (2015) Selecting Machine Learning Algorithms Using Regression Models. Selecting Machine Learning Algorithms Using Regression Models.
Frank Hutter, Jörg Lücke & Lars Schmidt-Thieme. (2015) Beyond Manual Tuning of Hyperparameters. KI - Künstliche Intelligenz 29:4, pages 329-337.
Crossref
Yiyong Feng & Daniel P. Palomar. (2015) Normalization of Linear Support Vector Machines. IEEE Transactions on Signal Processing 63:17, pages 4673-4688.
Crossref
Rafael M. O. Cruz, Robert Sabourin & George D. C. Cavalcanti. (2015) META-DES.H: A Dynamic Ensemble Selection technique using meta-learning and a dynamic weighting approach. META-DES.H: A Dynamic Ensemble Selection technique using meta-learning and a dynamic weighting approach.
Michel Ballings & Dirk Van den Poel. (2015) CRM in social media: Predicting increases in Facebook usage frequency. European Journal of Operational Research 244:1, pages 248-260.
Crossref
Rafael M.O. Cruz, Robert Sabourin, George D.C. Cavalcanti & Tsang Ing Ren. (2015) META-DES: A dynamic ensemble selection framework using meta-learning. Pattern Recognition 48:5, pages 1925-1935.
Crossref
Antonio Lavecchia. (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discovery Today 20:3, pages 318-331.
Crossref
Panagiotis C. Petrantonakis & Leontios J. Hadjileontiadis. 2015. Emotion Recognition. Emotion Recognition 269 293 .
Geert Gins, Pieter Van den Kerkhof, Jef Vanlaer & Jan F.M. Van Impe. (2015) Improving classification-based diagnosis of batch processes through data selection and appropriate pretreatment. Journal of Process Control 26, pages 90-101.
Crossref
Jens Meier, Andreas Dietz, Andreas Boehm & Thomas Neumuth. (2015) Predicting treatment process steps from events. Journal of Biomedical Informatics 53, pages 308-319.
Crossref
Sam Wuyts, Geert Gins, Pieter Van den Kerkhof & Jan Van Impe. (2015) Fault Identification in Batch Processes Using Process Data or Contribution Plots: A Comparative Study∗∗Work supported in part by Project PFV/10/002 (OPTEC Optimization in Engineering Center) of the Research Council of the KU Leuven, Project KP/09/005 (SCORES4CHEM) of the Industrial Research Council of the KU Leuven, and the Belgian Program on Interuniversity Poles of Attraction initiated by the Belgian Federal Science Policy Office. The authors assume scientific responsibility.. IFAC-PapersOnLine 48:8, pages 1282-1287.
Crossref
Geer Teng, Changzheng He & Xin Gu. (2014) Response model based on weighted bagging GMDH. Soft Computing 18:12, pages 2471-2484.
Crossref
Guangtao Wang, Qinbao Song, Xueying Zhang & Kaiyuan Zhang. (2014) A Generic Multilabel Learning-Based Classification Algorithm Recommendation Method. ACM Transactions on Knowledge Discovery from Data 9:1, pages 1-30.
Crossref
Dai Xinghang, Matta Nada & Ducellier Guillaume. (2014) Knowledge discovery in collaborative design projects. Knowledge discovery in collaborative design projects.
Matthias Reif & Faisal Shafait. (2014) Efficient feature size reduction via predictive forward selection. Pattern Recognition 47:4, pages 1664-1673.
Crossref
Douglas Rodrigues, Luís A.M. Pereira, Rodrigo Y.M. Nakamura, Kelton A.P. Costa, Xin-She Yang, André N. Souza & João Paulo Papa. (2014) A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest. Expert Systems with Applications 41:5, pages 2250-2258.
Crossref
M. Julia Flores, José A. Gámez & Ana M. Martínez. (2014) Domains of competence of the semi-naive Bayesian network classifiers. Information Sciences 260, pages 120-148.
Crossref
Manuel J.A. Eugster, Friedrich Leisch & Carolin Strobl. (2014) (Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithms. Computational Statistics & Data Analysis 71, pages 986-1000.
Crossref
Matthias Reif, Faisal Shafait, Markus Goldstein, Thomas Breuel & Andreas Dengel. (2012) Automatic classifier selection for non-experts. Pattern Analysis and Applications 17:1, pages 83-96.
Crossref
Xinghang Dai, Nada Matta & Guillaume Ducellier. 2014. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications 117 123 .
Xiaoheng Pan, Yangping Li, Xiaorui Wei & Huaqiang Yuan. 2014. Future Information Technology. Future Information Technology 255 260 .
Zhe Wang & Xiangyang Xue. 2014. Support Vector Machines Applications. Support Vector Machines Applications 23 48 .
Juzlinda Ghazali, Shahrul Azman Noah & Lailatulqadri Zakaria. (2013) Classification of Images for Automatic Textual Annotation: A Review of Techniques. Journal of Applied Sciences 13:6, pages 760-767.
Crossref
N. Tollenaar & P. G. M. van der Heijden. (2013) Which Method Predicts Recidivism Best?: A Comparison of Statistical, Machine Learning and Data Mining Predictive Models. Journal of the Royal Statistical Society Series A: Statistics in Society 176:2, pages 565-584.
Crossref
Abdallah Bashir Musa. (2012) Comparative study on classification performance between support vector machine and logistic regression. International Journal of Machine Learning and Cybernetics 4:1, pages 13-24.
Crossref
Sharareh R. Niakan Kalhori & Xiao-Jun Zeng. (2013) Evaluation and Comparison of Different Machine Learning Methods to Predict Outcome of Tuberculosis Treatment Course. Journal of Intelligent Learning Systems and Applications 05:03, pages 184-193.
Crossref
Sebastien Destercke. 2013. Advanced Information Systems Engineering. Advanced Information Systems Engineering 112 127 .
Veer Sain Dixit & Shveta Kundra Bhatia. 2013. Computational Science and Its Applications – ICCSA 2013. Computational Science and Its Applications – ICCSA 2013 498 512 .
Michel Ballings & Dirk Van den Poel. (2012) Customer event history for churn prediction: How long is long enough?. Expert Systems with Applications 39:18, pages 13517-13522.
Crossref
Darren M. Chitty. (2012) Fast parallel genetic programming: multi-core CPU versus many-core GPU. Soft Computing 16:10, pages 1795-1814.
Crossref
Qinbao Song, Guangtao Wang & Chao Wang. (2012) Automatic recommendation of classification algorithms based on data set characteristics. Pattern Recognition 45:7, pages 2672-2689.
Crossref
Satchidananda Dehuri, Rahul Roy, Sung-Bae Cho & Ashish Ghosh. (2012) An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification. Journal of Systems and Software 85:6, pages 1333-1345.
Crossref
Matthias Reif, Faisal Shafait & Andreas Dengel. (2012) Meta-learning for evolutionary parameter optimization of classifiers. Machine Learning 87:3, pages 357-380.
Crossref
Panagiotis C. Petrantonakis & Leontios J. Hadjileontiadis. (2012) Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain. IEEE Transactions on Signal Processing 60:5, pages 2604-2616.
Crossref
Domen Novak, Matjaž Mihelj & Marko Munih. (2012) A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. Interacting with Computers 24:3, pages 154-172.
Crossref
Xisheng He, Zhe Wang, Cheng Jin, Yingbin Zheng & Xiangyang Xue. (2012) A simplified multi-class support vector machine with reduced dual optimization. Pattern Recognition Letters 33:1, pages 71-82.
Crossref
Pardeep Kumar, Vivek Kumar Sehgal, Nitin & Durg Singh Chauhan. 2012. Advances in Communication, Network, and Computing. Advances in Communication, Network, and Computing 309 312 .
Pardeep Kumar, Nitin, Vivek Kumar Sehgal & Durg Singh Chauhan. 2012. Advances in Computer Science, Engineering & Applications. Advances in Computer Science, Engineering & Applications 673 682 .
Georgi Nalbantov, Patrick Groenen & Evgueni Smirnov. 2012. Reliable Knowledge Discovery. Reliable Knowledge Discovery 227 238 .
Pardeep Kumar, Nitin, Vivek Sehgal & Durg Singh Chauhan. (2011) Performance evaluation of decision tree versus artificial neural network based classifiers in diversity of datasets. Performance evaluation of decision tree versus artificial neural network based classifiers in diversity of datasets.
Nicola Segata & Enrico Blanzieri. (2010) Operators for transforming kernels into quasi-local kernels that improve SVM accuracy. Journal of Intelligent Information Systems 37:2, pages 155-186.
Crossref
P. C. Petrantonakis & L. J. Hadjileontiadis. (2011) A Novel Emotion Elicitation Index Using Frontal Brain Asymmetry for Enhanced EEG-Based Emotion Recognition. IEEE Transactions on Information Technology in Biomedicine 15:5, pages 737-746.
Crossref
Kai Li & Xiaoxia Lu. (2011) A Double Margin Based Fuzzy Support Vector Machine Algorithm. Journal of Computers 6:9.
Crossref
Janick V. Frasch, Aleksander Lodwich, Faisal Shafait & Thomas M. Breuel. (2011) A Bayes-true data generator for evaluation of supervised and unsupervised learning methods. Pattern Recognition Letters 32:11, pages 1523-1531.
Crossref
Dominik Heider, Jens Verheyen & Daniel Hoffmann. (2010) Predicting Bevirimat resistance of HIV-1 from genotype. BMC Bioinformatics 11:1.
Crossref
Anchana Khemphila & Veera Boonjing. (2010) Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients. Comparing performances of logistic regression, decision trees, and neural networks for classifying heart disease patients.
Hans Risselada, Peter C. Verhoef & Tammo H.A. Bijmolt. (2010) Staying Power of Churn Prediction Models. Journal of Interactive Marketing 24:3, pages 198-208.
Crossref
Zhao Lu, Lily Rui Liang, Gangbing Song & Shufang Wang. (2010) Polychotomous kernel Fisher discriminant via top–down induction of binary tree. Computers & Mathematics with Applications 60:3, pages 511-519.
Crossref
Pasi Luukka & Jouni Lampinen. 2010. Computational Intelligence in Optimization. Computational Intelligence in Optimization 263 283 .
Pardeep Kumar, Saroj & Rajesh Siddavatam. (2009) Classification Models: Non Evolutionary vs. Evolutionary Approach. Classification Models: Non Evolutionary vs. Evolutionary Approach.
H. Bouma, J.J. Sonnemans, A. Vilanova & F.A. Gerritsen. (2009) Automatic Detection of Pulmonary Embolism in CTA Images. IEEE Transactions on Medical Imaging 28:8, pages 1223-1230.
Crossref
T. Martinetz, K. Labusch & D. Schneegass. (2009) SoftDoubleMaxMinOver: Perceptron-Like Training of Support Vector Machines. IEEE Transactions on Neural Networks 20:7, pages 1061-1072.
Crossref
Jason C. Isaacs. (2009) Stochastic orthogonal and nonorthogonal subspace basis pursuit. Stochastic orthogonal and nonorthogonal subspace basis pursuit.
Hristo S. Nikolov, Doyno I. Petkov, Nina Jeliazkova, Stela Ruseva & Kiril Boyanov. (2009) Non-linear methods in remotely sensed multispectral data classification. Advances in Space Research 43:5, pages 859-868.
Crossref
Neeba N.V & C.V. Jawahar. (2009) Empirical Evaluation of Character Classification Schemes. Empirical Evaluation of Character Classification Schemes.
Guilherme Del Fiol & Peter J. Haug. (2009) Classification models for the prediction of clinicians’ information needs. Journal of Biomedical Informatics 42:1, pages 82-89.
Crossref
Jonghwa Kim & Elisabeth Andre. (2008) Emotion recognition based on physiological changes in music listening. IEEE Transactions on Pattern Analysis and Machine Intelligence 30:12, pages 2067-2083.
Crossref
Minh Ha Nguyen, H.A. Abbass & R.I. McKay. (2008) Analysis of CCME: Coevolutionary Dynamics, Automatic Problem Decomposition, and Regularization. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 38:1, pages 100-109.
Crossref
Imran Kurt, Mevlut Ture & A. Turhan Kurum. (2008) Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Systems with Applications 34:1, pages 366-374.
Crossref
Harris Papadopoulos, Volodya Vovk & Alex Gammermam. (2007) Conformal Prediction with Neural Networks. Conformal Prediction with Neural Networks.
Jason C. Isaacs, Simon Foo & Anke Meyer-Baese. (2007) Enhanced prediction of protein cellular localization sites with genetic algorithm optimal kernel projection analysis. Enhanced prediction of protein cellular localization sites with genetic algorithm optimal kernel projection analysis.
Emilio Carrizosa, Belén Martín-Barragán, Frank Plastria & Dolores Romero Morales. (2007) On the Selection of the Globally Optimal Prototype Subset for Nearest-Neighbor Classification. INFORMS Journal on Computing 19:3, pages 470-479.
Crossref
Ankur Srivastava, Andrew Meade & Jennifer Needham. (2007) Pilot Rating Classification of the HH-60H Sea Hawk Helicopter. Pilot Rating Classification of the HH-60H Sea Hawk Helicopter.
José Castro, Jimmy Secretan, Michael Georgiopoulos, Ronald DeMara, Georgios Anagnostopoulos & Avelino Gonzalez. (2007) Pipelining of Fuzzy ARTMAP without matchtracking: Correctness, performance bound, and Beowulf evaluation. Neural Networks 20:1, pages 109-128.
Crossref
Ruppa K. Thulasiram & Adenike Y. Bamgbade. 2007. Computational Intelligence in Economics and Finance. Computational Intelligence in Economics and Finance 145 155 .
Jose-federico Ramirez-Cruz, Olac Fuentes, Vicente Alarcon-Aquino & Luciano Garcia-Banuelos. (2006) Instance Selection and Feature Weighting Using Evolutionary Algorithms. Instance Selection and Feature Weighting Using Evolutionary Algorithms.
Georgi I. Nalbantov, Jan C. Bioch & Patrick J. F. Groenen. 2006. Machine Learning: ECML 2006. Machine Learning: ECML 2006 703 710 .
José Castro, Michael Georgiopoulos, Ronald Demara & Avelino Gonzalez. (2005) Data-partitioning using the Hilbert space filling curves: Effect on the speed of convergence of Fuzzy ARTMAP for large database problems. Neural Networks 18:7, pages 967-984.
Crossref
Paul R. Harper. (2005) A review and comparison of classification algorithms for medical decision making. Health Policy 71:3, pages 315-331.
Crossref
Stewart Massie, Susan Craw & Nirmalie Wiratunga. 2005. Applications and Innovations in Intelligent Systems XII. Applications and Innovations in Intelligent Systems XII 222 234 .
Jürgen Paetz. (2004) Reducing the number of neurons in radial basis function networks with dynamic decay adjustment. Neurocomputing 62, pages 79-91.
Crossref
David Meyer, Friedrich Leisch & Kurt Hornik. (2003) The support vector machine under test. Neurocomputing 55:1-2, pages 169-186.
Crossref
Nathaniel A. Woody & Steven D. Brown. (2003) Hybrid Bayesian networks: making the hybrid Bayesian classifier robust to missing training data. Journal of Chemometrics 17:5, pages 266-273.
Crossref
C. Marsala & B. Bouchon-Meunier. (2003) Choice of a method for the construction of fuzzy decision trees. Choice of a method for the construction of fuzzy decision trees.
J. Wang, B. Yu & L. Gasser. (2002) Concept tree based clustering visualization with shaded similarity matrices. Concept tree based clustering visualization with shaded similarity matrices.
Alex A. FreitasAlex A. Freitas. 2002. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Data Mining and Knowledge Discovery with Evolutionary Algorithms 13 43 .
Alex A. FreitasAlex A. Freitas. 2002. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Data Mining and Knowledge Discovery with Evolutionary Algorithms 255 261 .
David Braendler & Tim Hendtlass. 2002. Developments in Applied Artificial Intelligence. Developments in Applied Artificial Intelligence 190 199 .
Kostas Proedrou, Ilia Nouretdinov, Volodya Vovk & Alex Gammerman. 2002. Machine Learning: ECML 2002. Machine Learning: ECML 2002 381 390 .
David J. Hand & Keming Yu. (2007) Idiot's Bayes—Not So Stupid After All?. International Statistical Review 69:3, pages 385-398.
Crossref
Henry Brighton & Chris Mellish. 2001. Instance Selection and Construction for Data Mining. Instance Selection and Construction for Data Mining 77 94 .
N. Ye, Q. Zhong & G.E. Rahn. (2000) Confidence assessment of quality prediction from process measurement in sequential manufacturing processes. IEEE Transactions on Electronics Packaging Manufacturing 23:3, pages 177-184.
Crossref
Alex A. Freitas. (2000) Understanding the crucial differences between classification and discovery of association rules. ACM SIGKDD Explorations Newsletter 2:1, pages 65-69.
Crossref
. (2000) 10.1162/153244304322972694. CrossRef Listing of Deleted DOIs 1.
Crossref
Ross D. King, Mohammed Ouali, Arbra T. Strong, Alaaeldin Aly, Adel Elmaghraby, Mehmed Kantardzic & David Page. (2000) Is it better to combine predictions?. Protein Engineering, Design and Selection 13:1, pages 15-19.
Crossref
Ryan Benton, Miroslav Kubat & Rasaiah Loganantharaj. 2000. Intelligent Problem Solving. Methodologies and Approaches. Intelligent Problem Solving. Methodologies and Approaches 434 442 .
Elizabeth McKenna & Barry Smyth. 2000. Advances in Case-Based Reasoning. Advances in Case-Based Reasoning 186 197 .
Olivier Gascuel, Bernadette Bouchon-Meunier, Gilles Caraux, Patrick Gallinari, Alain Guénoche, Yann Guermeur, Yves Lechevallier, Christophe Marsala, Laurent Miclet, Jacques Nicolas, Richard Nock, Mohammed Ramdani, Michèle Sebag, Basavanneppa Tallur, Gilles Venturini & Patrick Vitte. (2011) Twelve Numerical, Symbolic and Hybrid Supervised Classification Methods. International Journal of Pattern Recognition and Artificial Intelligence 12:05, pages 517-571.
Crossref
Jesus Mena. (2007) Machine‐learning the business: Using data mining for competitive intelligence. Competitive Intelligence Review 7:4, pages 18-25.
Crossref
Ross D. King & Michael J.E. Sternberg. (2008) Identification and application of the concepts important for accurate and reliable protein secondary structure prediction. Protein Science 5:11, pages 2298-2310.
Crossref
Zhongmin Luo & Raymond Brummelhuis. (2018) CDS Rate Construction Methods by Machine Learning Techniques (Presentation Slides). SSRN Electronic Journal.
Crossref
Raymond Brummelhuis & Zhongmin Luo. (2017) CDS Rate Construction Methods by Machine Learning Techniques. SSRN Electronic Journal.
Crossref

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.