A method of combining multiple probabilistic classifiers through soft competition on different feature sets

Ke Chen, Huisheng Chi

    Research output: Contribution to journalArticlepeer-review

    Abstract

    A novel method is proposed for combining multiple probabilistic classifiers on different feature sets. In order to achieve the improved classification performance, a generalized finite mixture model is proposed as a linear combination scheme and implemented based on radial basis function networks. In the linear combination scheme, soft competition on different feature sets is adopted as an automatic feature rank mechanism so that different feature sets can be always simultaneously used in an optimal way to determine linear combination weights. For training the linear combination scheme, a learning algorithm is developed based on Expectation-Maximization (EM) algorithm. The proposed method has been applied to a typical real-world problem, viz., speaker identification, in which different feature sets often need consideration simultaneously for robustness. Simulation results show that the proposed method yields good performance in speaker identification.
    Original languageEnglish
    Pages (from-to)227-252
    Number of pages25
    JournalNeurocomputing
    Volume20
    Issue number1-3
    DOIs
    Publication statusPublished - 31 Aug 1998

    Keywords

    • Combination of multiple classifiers
    • Different feature sets
    • Expectation-maximization (EM) algorithm
    • Soft competition
    • Speaker identification

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