NeC4.5: Neural Ensemble Based C4.5. Decision tree is with good comprehensibility while neural network ensemble is with strong generalization ability. In this paper, these merits are integrated into a novel decision tree algorithm NeC4.5. This algorithm trains a neural network ensemble at first. Then, the trained ensemble is employed to generate a new training set through replacing the desired class labels of the original training examples with those output from the trained ensemble. Some extra training examples are also generated from the trained ensemble and added to the new training set. Finally, a C4.5 decision tree is grown from the new training set. Since its learning results are decision trees, the comprehensibility of NeC4.5 is better than that of neural network ensemble. Moreover, experiments show that the generalization ability of NeC4.5 decision trees can be better than that of C4.5 decision trees.

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Cheng, Yichen; Wang, Xinlei; Xia, Yusen: Supervised (t)-distributed stochastic neighbor embedding for data visualization and classification (2021)
  2. Bai, Yanqin; Han, Xiao; Chen, Tong; Yu, Hua: Quadratic kernel-free least squares support vector machine for target diseases classification (2015)
  3. Rokach, Lior; Maimon, Oded: Data mining with decision trees. Theory and applications. (2015)
  4. Yu, Xu; Yu, Miao; Xu, Li-xun; Yang, Jing; Xie, Zhi-qiang: Training classifiers under covariate shift by constructing the maximum consistent distribution subset (2015)
  5. Yu, Zhiwen; Li, Le; Wong, Hau-San; You, Jane; Han, Guoqiang; Gao, Yunjun; Yu, Guoxian: Probabilistic cluster structure ensemble (2014) ioport
  6. Zhang, Min-Ling; Zhou, Zhi-Hua: Exploiting unlabeled data to enhance ensemble diversity (2013)
  7. Walczak, Steven: Methodological triangulation using neural networks for business research (2012) ioport
  8. Chen, Jie; Chen, Xilin; Yang, Jie; Shan, Shiguang; Wang, Ruiping; Gao, Wen: Optimization of a training set for more robust face detection (2009)
  9. Rokach, Lior: Taxonomy for characterizing ensemble methods in classification tasks: a review and annotated bibliography (2009)
  10. Winkler, Stephan M.; Affenzeller, Michael; Wagner, Stefan: Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis (2009) ioport
  11. Hu, Qinghua; Yu, Daren; Xie, Zongxia; Li, Xiaodong: EROS: Ensemble rough subspaces (2007)
  12. Zhou, Zhi-Hua; Jiang, Yuan: NeC4.5: Neural Ensemble Based C4.5 (2004) ioport