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On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods

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Yüksel Özkan, Mert Demirarslan & Aslı Suner. (2022) Effect of data preprocessing on ensemble learning for classification in disease diagnosis. Communications in Statistics - Simulation and Computation 0:0, pages 1-21.
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Irfan Javid, Rozaida Ghazali, Muhammad Zulqarnain & Norlida Hassan. (2023) Data pre-processing for cardiovascular disease classification: A systematic literature review. Journal of Intelligent & Fuzzy Systems 44:1, pages 1525-1545.
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José A. Sáez. (2022) Noise Models in Classification: Unified Nomenclature, Extended Taxonomy and Pragmatic Categorization. Mathematics 10:20, pages 3736.
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Irfan Javid, Ahmed Khalaf Zager Alsaedi, Rozaida Ghazali, Yana Mazwin Mohmad Hassim & Muhammad Zulqarnain. (2022) Optimally organized GRU-deep learning model with Chi2 feature selection for heart disease prediction. Journal of Intelligent & Fuzzy Systems 42:4, pages 4083-4094.
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Ian A. Scott. (2021) Demystifying machine learning: a primer for physicians. Internal Medicine Journal 51:9, pages 1388-1400.
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Juan Martin, Jose A. Saez & Emilio Corchado. (2021) On the Regressand Noise Problem: Model Robustness and Synergy With Regression-Adapted Noise Filters. IEEE Access 9, pages 145800-145816.
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Lucas Lo Vercio, Kimberly Amador, Jordan J Bannister, Sebastian Crites, Alejandro Gutierrez, M. Ethan MacDonald, Jasmine Moore, Pauline Mouches, Deepthi Rajashekar, Serena Schimert, Nagesh Subbanna, Anup Tuladhar, Nanjia Wang, Matthias Wilms, Anthony Winder & Nils D Forkert. (2020) Supervised machine learning tools: a tutorial for clinicians. Journal of Neural Engineering 17:6, pages 062001.
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H. Benhar, A. Idri & J.L. Fernández-Alemán. (2020) Data preprocessing for heart disease classification: A systematic literature review. Computer Methods and Programs in Biomedicine 195, pages 105635.
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Maryam Samami, Ebrahim Akbari, Moloud Abdar, Pawel Plawiak, Hossein Nematzadeh, Mohammad Ehsan Basiri & Vladimir Makarenkov. (2020) A mixed solution-based high agreement filtering method for class noise detection in binary classification. Physica A: Statistical Mechanics and its Applications 553, pages 124219.
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Haizhen Zhu, Mingqing Xiao, Xin Zhao, Xilang Tang, Longhao Yang, Weijie Kang & Zhaozheng Liu. (2020) A structure optimization method for extended belief-rule-based classification system. Knowledge-Based Systems 203, pages 106096.
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Hamideh Hajiabadi, Vahide Babaiyan, Davood Zabihzadeh & Moein Hajiabadi. (2020) Combination of loss functions for robust breast cancer prediction. Computers & Electrical Engineering 84, pages 106624.
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Haizhen Zhu, Mingqing Xiao, Longhao Yang, Xilang Tang, Yajun Liang & Jianfeng Li. (2020) A minimum centre distance rule activation method for extended belief rule-based classification systems. Applied Soft Computing 91, pages 106214.
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Junhua Ding, Xinchuan Li, Xiaojun Kang & Venkat N. Gudivada. (2019) A Case Study of the Augmentation and Evaluation of Training Data for Deep Learning. Journal of Data and Information Quality 11:4, pages 1-22.
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Zerhari Btissam, Ait Lahcen Ayoub & Mouline Salma. (2019) MIPCNF: Multi-iterative partitioning class noise filter. Journal of Intelligent & Fuzzy Systems, pages 1-12.
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Luís P.F. Garcia, Jens Lehmann, André C.P.L.F. de Carvalho & Ana C. Lorena. (2019) New label noise injection methods for the evaluation of noise filters. Knowledge-Based Systems 163, pages 693-704.
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A. Idri, H. Benhar, J.L. Fernández-Alemán & I. Kadi. (2018) A systematic map of medical data preprocessing in knowledge discovery. Computer Methods and Programs in Biomedicine 162, pages 69-85.
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Junhua Ding, XinChuan Li & Venkat N. Gudivada. (2017) Augmentation and evaluation of training data for deep learning. Augmentation and evaluation of training data for deep learning.
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Junhua Ding, Jiabin Wang, Xiaojun Kang & Xin-Hua Hu. (2017) Building an SVM Classifier for Automated Selection of Big Data. Building an SVM Classifier for Automated Selection of Big Data.

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