Statistical discriminability estimation for pattern classification based on neural incremental attribute learning

Ting Wang*, Sadasivan Puthusserypady, Sheng Uei Guan, Prudence W.H. Wong

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingChapterpeer-review

Abstract

Feature ordering is a significant data preprocessing method in Incremental Attribute Learning (IAL), a novel machine learning approach which gradually trains features according to a given order. Previous research has shown that, similar to feature selection, feature ordering is also important based on each feature's discrimination ability, and should be sorted in a descending order of their discrimination ability. However, such an ordering is crucial for the performance of IAL. As the number of feature dimensions in IAL is increasing, feature discrimination ability also should be calculated in the corresponding incremental way. Based on Single Discriminability (SD), where only the feature discrimination ability is computed, a new filter statistical feature discrimination ability predictive metric, called the Accumulative Discriminability (AD), is designed for the dynamical feature discrimination ability estimation. Moreover, a criterion that summarizes all the produced values of AD is employed with a GA (Genetic Algorithm)- based approach to obtain the optimum feature ordering for classification problems based on neural networks by means of IAL. Compared with the feature ordering obtained by other approaches, the method proposed in this paper exhibits better performance in the final classification results. Such a phenomenon indicates that, (i) the feature discrimination ability should be incrementally estimated in IAL, and (ii) the feature ordering derived by AD and its corresponding approaches are applicable with IAL.

Original languageEnglish
Title of host publicationArtificial Intelligence
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global
Pages2048-2070
Number of pages23
Volume3
ISBN (Electronic)9781522517603
ISBN (Print)1522517596, 9781522517597
DOIs
Publication statusPublished - 12 Dec 2016

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