Generalized fisher score for feature selection

Quanquan Gu, Zhenhui Li, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

330 Scopus citations

Abstract

Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which leads to a suboptimal subset of features. In this paper, we present a generalized Fisher score to jointly select features. It aims at finding an subset of features, which maximize the lower bound of traditional Fisher score. The resulting feature selection problem is a mixed integer programming, which can be reformulated as a quadratically constrained linear programming (QCLP). It is solved by cutting plane algorithm, in each iteration of which a multiple kernel learning problem is solved alternatively by multivariate ridge regression and projected gradient descent. Experiments on benchmark data sets indicate that the proposed method outperforms Fisher score as well as many other state-of-the-art feature selection methods.

Original languageEnglish (US)
Title of host publicationProceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011
PublisherAUAI Press
Pages266-273
Number of pages8
StatePublished - 2011

Publication series

NameProceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Applied Mathematics

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