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Abstract and Applied Analysis
Volume 2014, Article ID 376950, 6 pages
http://dx.doi.org/10.1155/2014/376950
Research Article

A Weighted Voting Classifier Based on Differential Evolution

School of Computer and Information Technology, Liaoning Normal University, Dalian 116081, China

Received 8 April 2014; Accepted 12 May 2014; Published 22 May 2014

Academic Editor: Caihong Li

Copyright © 2014 Yong Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [11 citations]

The following is the list of published articles that have cited the current article.

  • Rui Li, and Xiaodan Wang, “Self-adaptive weighted majority vote algorithm based on entropy,” 2017 2nd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), pp. 73–77, . View at Publisher · View at Google Scholar
  • Mohammad Nazmul Haque, M Nasimul Noman, Regina Berretta, and Pablo Moscato, “Optimising weights for heterogeneous ensemble of classifiers with differential evolution,” 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 233–240, . View at Publisher · View at Google Scholar
  • Qiang Liu, Hailin Zhang, and Yanbo Ma, “Reliability Evaluation for Wireless Sensor Network Based on Weighted Voting System with Unreliable Links,” 2016 3rd International Conference on Information Science and Control Engineering (ICISCE), pp. 1384–1388, . View at Publisher · View at Google Scholar
  • Yingli Yao, “Library Resource Vertical Search Engine Based on Ontology,” 2017 International Conference on Smart Grid and Electrical Automation (ICSGEA), pp. 672–675, . View at Publisher · View at Google Scholar
  • Florin Leon, Sabina-Adriana Floria, and Costin Badica, “Evaluating the effect of voting methods on ensemble-based classification,” 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), pp. 1–6, . View at Publisher · View at Google Scholar
  • Aytuğ Onan, Serdar Korukoğlu, and Hasan Bulut, “A Multiobjective Weighted Voting Ensemble Classifier Based on Differential Evolution Algorithm for Text Sentiment Classification,” Expert Systems with Applications, 2016. View at Publisher · View at Google Scholar
  • Mouaz Al-Mallah, Steven Keteyian, Jonathan Ehrman, Manal Alghamdi, Clinton Brawner, and Sherif Sakr, “Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project,” PLoS ONE, vol. 12, no. 7, 2017. View at Publisher · View at Google Scholar
  • Qiang Liu, and Hailin Zhang, “Weighted Voting System With Unreliable Links,” IEEE Transactions on Reliability, vol. 66, no. 2, pp. 339–350, 2017. View at Publisher · View at Google Scholar
  • Chuanbo Huang, “Terrain classification of polarimetric synthetic aperture radar imagery based on polarimetric features and ensemble learning,” Journal of Applied Remote Sensing, vol. 11, no. 2, 2017. View at Publisher · View at Google Scholar
  • Xiaodan Wang, Rui Li, Aijun Xue, and Xiangfang Sun, “HRRP fusion recognition by a self-adaptive weighted majority vote strategy based on entropy,” Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, vol. 39, no. 4, pp. 707–713, 2017. View at Publisher · View at Google Scholar
  • Yong Zhang, Bo Liu, and Jiaxin Yu, “A selective ensemble learning approach based on evolutionary algorithm,” Journal of Intelligent and Fuzzy Systems, vol. 32, no. 3, pp. 2365–2373, 2017. View at Publisher · View at Google Scholar