文献类型：期刊文献

英文题名：Integrated Fisher linear discriminants: An empirical study

作者：Gao Daqi[1];Ding Jun[1];Zhu Changming[1]

机构：[1]E China Univ Sci & Technol, Dept Comp Sci, State Key Lab Bioreactor Engn, Shanghai 200237, Peoples R China

年份：2014

卷号：47

期号：2

起止页码：789

外文期刊名：PATTERN RECOGNITION

收录：;EI(收录号：20134516958073);WOS:【SCI-EXPANDED(收录号:WOS:000329413000025)】；

基金：This work is funded by the National Science Foundation of China (NSFC) under Grant nos. 21176077, 61272198 and 60675027, the High-Tech Development Program of China (863) under Grant no. 2006AA10Z315, and the Open Funding Project of the State Key Laboratory of Bioreactor Engineering.

语种：英文

外文关键词：Fisher linear discriminants; Imbalanced datasets; Empirical thresholds; Neighborhood-preserving transformations; Iterative learning

摘要：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. (C) 2013 Elsevier Ltd. All rights reserved.