Integrated Fisher linear discriminants: An empirical study

Authors:
Gao Daqi;Ding Jun;Zhu Changming
Affiliations:
-;-;-
Venue:
Pattern Recognition
Year:
2014

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Abstract

This paper studies Fisher linear discriminants (FLDs) based on classification accuracies for imbalanced datasets. An optimal threshold is found out from a series of empirical formulas developed, which is related not only to sample sizes but also to distribution regions. A mixed binary-decimal coding system is suggested to make the very dense datasets sparse and enlarge the class margins on condition that the neighborhood relationships of samples are nearly preserved. The within-class scatter matrices being or approximately singular should be moderately reduced in dimensionality but not added with tiny perturbations. The weight vectors can be further updated by a kind of epoch-limited (three at most) iterative learning strategy provided that the current training error rates come down accordingly. Putting the above ideas together, this paper proposes a type of integrated FLDs. The extensive experimental results over real-world datasets have demonstrated that the integrated FLDs have obvious advantages over the conventional FLDs in the aspects of learning and generalization performances for the imbalanced datasets.