Probabilistic feature selection and classification vector machine

Bingbing JIANG, Chang LI, Maarten DE RIJKE, Xin YAO, Huanhuan CHEN

Research output: Journal PublicationsJournal Article (refereed)peer-review

29 Citations (Scopus)

Abstract

Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose a subset of relevant samples from the input data and make reliable probabilistic predictions. However, in the presence of high-dimensional data with irrelevant features, traditional sparse Bayesian classifiers suffer from performance degradation and low efficiency due to the incapability of eliminating irrelevant features. To tackle this problem, we propose a novel sparse Bayesian embedded feature selection algorithm that adopts truncated Gaussian distributions as both sample and feature priors. The proposed algorithm, called probabilistic feature selection and classification vector machine (PFCVMLP) is able to simultaneously select relevant features and samples for classification tasks. In order to derive the analytical solutions, Laplace approximation is applied to compute approximate posteriors and marginal likelihoods. Finally, parameters and hyperparameters are optimized by the type-II maximum likelihood method. Experiments on three datasets validate the performance of PFCVMLP along two dimensions: classification performance and effectiveness for feature selection. Finally, we analyze the generalization performance and derive a generalization error bound for PFCVMLP. By tightening the bound, the importance of feature selection is demonstrated. © 2019 Association for Computing Machinery.
Original languageEnglish
Article number21
JournalACM Transactions on Knowledge Discovery from Data
Volume13
Issue number2
Early online date19 Apr 2019
DOIs
Publication statusPublished - 30 Apr 2019
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China (Grant nos. 91846111 and 91746209), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant no. ZDSYS201703031748284), Ahold Delhaize, Amsterdam Data Science, the Bloomberg Research Grant program, the China Scholarship Council, the Criteo Faculty Research Award program, Elsevier, the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement nr 312827 (VOX-Pol), the Google Faculty Research Awards program, the Microsoft Research Ph.D. program, the Netherlands Institute for Sound and Vision, the Netherlands Organisation for Scientific Research (NWO) under project nrs CI-14-25, 652.002.001, 612.001.551, 652.001.003, and Yandex. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors.

Keywords

  • EEG emotion recognition
  • Feature selection
  • Probabilistic classification model
  • Sparse Bayesian learning
  • Supervised learning

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