488
Views
0
CrossRef citations to date
0
Altmetric
Articles

L-measure evaluation metric for fake information detection models with binary class imbalance

, ORCID Icon, , & ORCID Icon
Pages 1587-1606 | Received 29 Dec 2019, Accepted 16 Sep 2020, Published online: 05 Oct 2020

References

  • Adeniyi, D. A., Z. Wei, and Y. Yang. 2017. “Personalised News Filtering and Recommendation System Using Chi-square Statistics-based K-nearest Neighbour (χ 2SB-KNN) Model.” Enterprise Information Systems 11 (9): 1283–1316.
  • Al Najada, H., and X. Zhu. 2014. “iSRD: Spam Review Detection with Imbalanced Data Distributions.” In Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014), California, August (pp. 553–560). IEEE.
  • Almeida, T. A., J. M. G. Hidalgo, and A. Yamakami. 2011. “Contributions to the Study of SMS Spam Filtering: New Collection and Results.” In Proceedings of the 11th ACM symposium on Document engineering, California, September (pp. 259–262).
  • Alpaydin, E. 2020. Introduction to Machine Learning. Cambridge, Massachusetts, USA: MIT press.
  • Andrea, D. P., G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi. 2017. “Credit Card Fraud Detection: A Realistic Modeling and a Novel Learning Strategy.” IEEE Transactions on Neural Networks and Learning Systems 29 (8): 3784–3797.
  • Boughorbel, S., F. Jarray, and M. El-Anbari. 2017. “Optimal Classifier for Imbalanced Data Using Matthews Correlation Coefficient Metric.” PloS One 12 (6): e0177678. doi:https://doi.org/10.1371/journal.pone.0177678.
  • Brzezinski, D., and J. Stefanowski. 2013. “Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm.” IEEE Transactions on Neural Networks and Learning Systems 25 (1): 81–94. doi:https://doi.org/10.1109/TNNLS.2013.2251352.
  • Byvatov, E., and G. Schneider. 2003. “Support Vector Machine Applications in Bioinformatics.” Applied Bioinformatics 2 (2): 67–77.
  • Chang, J., J. Lefferman, C. Pedersen, and G. Martz. 2016. “When Fake News Stories Make Real News Headlines.” Nightline. ABC News.
  • Cheng, J., J. Chen, Y.-N. Guo, S. Cheng, L. Yang, and P. Zhang. 2019. “Adaptive CCR-ELM with Variable-length Brain Storm Optimization Algorithm for Class-imbalance Learning.” Natural Computing 1–12. doi:https://doi.org/10.1007/s11047-019-09735-9.
  • Chien, C.-F., R. Kerh, K.-Y. Lin, and A. P.-I. Yu. 2016. “Data-driven Innovation to Capture User-experience Product Design: An Empirical Study for Notebook Visual Aesthetics Design.” Computers & Industrial Engineering 99: 162–173. doi:https://doi.org/10.1016/j.cie.2016.07.006.
  • Chien, C.-F., K.-Y. Lin, and A. P.-I. Yu. 2014. “User-experience of Tablet Operating System: An Experimental Investigation of Windows 8, iOS 6, and Android 4.2.” Computers & Industrial Engineering 73: 75–84. doi:https://doi.org/10.1016/j.cie.2014.04.015.
  • Dwiyanti, E., Adiwijaya, A. Ardiyanti. 2016. “Handling Imbalanced Data in Churn Prediction Using Rusboost and Feature Selection (Case Study: Pt. Telekomunikasi Indonesia Regional 7).” In International Conference on Soft Computing and Data Mining, Indonesia, August (pp. 376–385). Springer.
  • Forman, G. 2006. “Tackling Concept Drift by Temporal Inductive Transfer.” In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, Washington, August (pp. 252–259). ACM.
  • Franke, J., F. Charoy, and P. E. Khoury. 2013. “Framework for Coordination of Activities in Dynamic Situations.” Enterprise Information Systems 7 (1): 33–60. doi:https://doi.org/10.1080/17517575.2012.690891.
  • García, V., A. I. Marqués, and J. S. Sánchez. 2019. “Exploring the Synergetic Effects of Sample Types on the Performance of Ensembles for Credit Risk and Corporate Bankruptcy Prediction.” Information Fusion 47: 88–101.
  • Huang, G.-B., H. Zhou, X. Ding, and R. Zhang. 2011. “Extreme Learning Machine for Regression and Multiclass Classification.” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 42 (2): 513–529. doi:https://doi.org/10.1109/TSMCB.2011.2168604.
  • Huang, G.-B., Q.-Y. Zhu, and C.-K. Siew. 2006. “Extreme Learning Machine: Theory and Applications.” Neurocomputing 70 (1–3): 489–501. doi:https://doi.org/10.1016/j.neucom.2005.12.126.
  • Lin, K.-Y. 2018. “User Experience-based Product Design for Smart Production to Empower Industry 4.0 In the Glass Recycling Circular Economy.” Computers & Industrial Engineering 125: 729–738. doi:https://doi.org/10.1016/j.cie.2018.06.023.
  • Lin, K.-Y., C.-F. Chien, and R. Kerh. 2016. “UNISON Framework of Data-driven Innovation for Extracting User Experience of Product Design of Wearable Devices.” Computers & Industrial Engineering 99: 487–502. doi:https://doi.org/10.1016/j.cie.2016.05.023.
  • Lin, K.-Y., A. P.-I. Yu, P.-C. Chu, and C.-F. Chien. 2017. “User-experience- Based Design of Experiments for New Product Development of Consumer Electronics and an Empirical Study.” Journal of Industrial and Production Engineering 34 (7): 504–519. doi:https://doi.org/10.1080/21681015.2017.1363089.
  • Martin-Diaz, I., D. Morinigo-Sotelo, O. Duque-Perez, and R. D. J. Romero- Troncoso. 2016. “Early Fault Detection in Induction Motors Using AdaBoost with Imbalanced Small Data and Optimized Sampling.” IEEE Transactions on Industry Applications 53 (3): 3066–3075. doi:https://doi.org/10.1109/TIA.2016.2618756.
  • Metsis, V., I. Androutsopoulos, and G. Paliouras. 2006. “Spam Filtering with Naive Bayes-which Naive Bayes?” In the Third Conference on Email and Anti-Spam, California, July (pp. 29–69). Gordon V. Cormack.
  • More, A. 2016. “Survey of Resampling Techniques for Improving Classification Performance in Unbalanced Datasets.” arXiv Preprint arXiv:1608.06048.
  • Pacepa, I. M., and R. J. Rychlak. 2013. Disinformation: Former Spy Chief Reveals Secret Strategy for Undermining Freedom, Attacking Religion, and Promoting Terrorism. Washinton, USA: Wnd Books.
  • Polikar, R., L. Upda, S. S. Upda, and V. Honavar. 2001. “Learn++: An Incremental Learning Algorithm for Supervised Neural Networks.” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 31 (4): 497–508. doi:https://doi.org/10.1109/5326.983933.
  • Powers, D. M. 2011. “Evaluation: From Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation.”
  • Raghuwanshi, B. S., and S. Shukla. 2018. “Class-specific Extreme Learning Machine for Handling Binary Class Imbalance Problem.” Neural Networks 105: 206–217. doi:https://doi.org/10.1016/j.neunet.2018.05.011.
  • Ratadiya, P., and R. Moorthy. 2019. “Spam Filtering on Forums: A Synthetic Oversampling Based Approach for Imbalanced Data Classification.” arXiv Preprint arXiv:1909.04826.
  • Sahoo, S. R., and B. B. Gupta. 2019. “Classification of Various Attacks and Their Defence Mechanism in Online Social Networks: A Survey.” Enterprise Information Systems 13 (6): 832–864. doi:https://doi.org/10.1080/17517575.2019.1605542.
  • Senadheera, V., M. Warren, and S. Leitch. 2017. “Social Media as an Information System: Improving the Technological Agility.” Enterprise Information Systems 11 (4): 512–533. doi:https://doi.org/10.1080/17517575.2016.1245872.
  • Shan, S., X. Liu, Y. Wei, L. Xu, B. Zhang, and L. Yu. 2020. “A New Emergency Management Dynamic Value Assessment Model Based on Social Media Data: A Multiphase Decision-making Perspective.” Enterprise Information Systems 14 (5): 680–709. doi:https://doi.org/10.1080/17517575.2020.1722251.
  • Shen, F., X. Zhao, Z. Li, K. Li, and Z. Meng. 2019. “A Novel Ensemble Classification Model Based on Neural Networks and A Classifier Optimisation Technique for Imbalanced Credit Risk Evaluation.” Physica A: Statistical Mechanics and Its Applications 526: 121073. doi:https://doi.org/10.1016/j.physa.2019.121073.
  • Sipos, R., D. Fradkin, F. Moerchen, and Z. Wang. 2014. “Log-based Predictive Maintenance.” In Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, New York, August (pp. 1867–1876). ACM.
  • Street, W. N., and Y. Kim. 2001. “A Streaming Ensemble Algorithm (SEA) for Large- Scale Classification.” In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, California, August (pp. 377–382). ACM.
  • Sun, J., H. Li, P.-C. Chang, and K.-Y. He. 2016. “The Dynamic Financial Distress Prediction Method of EBW-VSTW-SVM.” Enterprise Information Systems 10 (6): 611–638. doi:https://doi.org/10.1080/17517575.2014.986214.
  • Sun, J., H. Li, H. Fujita, B. Fu, and W. Ai. 2020. “Class-imbalanced Dynamic Financial Distress Prediction Based on Adaboost-SVM Ensemble Combined with SMOTE and Time Weighting.” Information Fusion 54: 128–144. doi:https://doi.org/10.1016/j.inffus.2019.07.006.
  • Sun, Y., K. Tang, Z. Zhu, and X. Yao. 2018. “Concept Drift Adaptation by Exploiting Historical Knowledge.” IEEE Transactions on Neural Networks and Learning Systems 29 (10): 4822–4832. doi:https://doi.org/10.1109/TNNLS.2017.2775225.
  • Tan, W., W. Xu, F. Yang, L. Xu, and C. Jiang. 2013. “A Framework for Service Enterprise Workflow Simulation with Multi-agents Cooperation.” Enterprise Information Systems 7 (4): 523–542. doi:https://doi.org/10.1080/17517575.2012.660503.
  • Tsymbal, A., M. Pechenizkiy, P. Cunningham, and S. Puuronen. 2008. “Dynamic Integration of Classifiers for Handling Concept Drift.” Information Fusion 9 (1): 56–68. doi:https://doi.org/10.1016/j.inffus.2006.11.002.
  • Wang, Z., J. Poon, and S. Poon. 2019. “AMI-Net+: A Novel Multi-Instance Neural Network for Medical Diagnosis from Incomplete and Imbalanced Data.” arXiv Preprint arXiv:1907.01734.
  • Zhang, J., X. Cai, T. Le, W. Fei, and F. Ma. 2019. “A Study on Effective Measurement of Search Results from Search Engines.” Journal of Global Information Management (JGIM) 27 (1): 196–221. doi:https://doi.org/10.4018/JGIM.2019010110.
  • Zhang, W., X. Tian, and W. He. 2019. “Information Seeking and Online Deal Seeking Behavior.” Journal of Global Information Management (JGIM) 27 (4): 147–160. doi:https://doi.org/10.4018/JGIM.2019100107.
  • Zhou, Z.-H. 2015. “Ensemble Learning.” In Encyclopedia of Biometrics, 411–416. Boston, MA: Springer. doi:https://doi.org/10.1007/978-1-4899-7488-4_293.RIS.
  • Žliobaitė, I., M. Pechenizkiy, and J. Gama. 2016. “An Overview of Concept Drift Applications.” In Big Data Analysis: New Algorithms for a New Society, 91–114. Cham: Springer. doi:https://doi.org/10.1007/978-3-319-26989-4_4.
  • Zong, W., G.-B. Huang, and Y. Chen. 2013. “Weighted Extreme Learning Machine for Imbalance Learning.” Neurocomputing 101: 229–242. doi:https://doi.org/10.1016/j.neucom.2012.08.010.

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.