Ensemble Feature Weighting Based on Local Learning and Diversity

Authors

  • Yun Li Nanjing University of Posts and Telecommunications
  • Suyan Gao Nanjing University of Posts and Telecommunications
  • Songcan Chen Nanjing University of Aeronautics and Astronautics

DOI:

https://doi.org/10.1609/aaai.v26i1.8275

Keywords:

Feature Selection, Ensemble

Abstract

Recently, besides the performance, the stability (robustness, i.e., the variation in feature selection results due to small changes in the data set) of feature selection is received more attention. Ensemble feature selection where multiple feature selection outputs are combined to yield more robust results without sacrificing the performance is an effective method for stable feature selection. In order to make further improvements of the performance (classification accuracy), the diversity regularized ensemble feature weighting framework is presented, in which the base feature selector is based on local learning with logistic loss for its robustness to huge irrelevant features and small samples. At the same time, the sample complexity of the proposed ensemble feature weighting algorithm is analyzed based on the VC-theory. The experiments on different kinds of data sets show that the proposed ensemble method can achieve higher accuracy than other ensemble ones and other stable feature selection strategy (such as sample weighting) without sacrificing stability

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Published

2021-09-20

How to Cite

Li, Y., Gao, S., & Chen, S. (2021). Ensemble Feature Weighting Based on Local Learning and Diversity. Proceedings of the AAAI Conference on Artificial Intelligence, 26(1), 1019-1025. https://doi.org/10.1609/aaai.v26i1.8275

Issue

Section

AAAI Technical Track: Machine Learning