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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 7369137, 9 pages
Research Article

The Hybrid Feature Selection Algorithm Based on Maximum Minimum Backward Selection Search Strategy for Liver Tissue Pathological Image Classification

Software College, Northeastern University, Shenyang 110819, China

Received 15 April 2016; Accepted 1 July 2016

Academic Editor: Xiaoqi Zheng

Copyright © 2016 Huiling Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose a novel feature selection algorithm for liver tissue pathological image classification. To improve the efficiency of feature selection, the same feature values of positive and negative samples are removed in rough selection. To obtain the optimal feature subset, a new heuristic search algorithm, which is called Maximum Minimum Backward Selection (MMBS), is proposed in precise selection. MMBS search strategy has the following advantages. (1) For the deficiency of Discernibility of Feature Subsets (DFS) evaluation criteria, which makes the class of small samples invalid for unbalanced samples, the Weighted Discernibility of Feature Subsets (WDFS) evaluation criteria are proposed as the evaluation strategy of MMBS, which is also available for unbalanced samples. (2) For the deficiency of Sequential Forward Selection (SFS) and Sequential Backward Selection (SBS), which can only add or only delete feature, MMBS decides whether to add the feature to feature subset according to WDFS criteria for each feature firstly; then it decides whether to remove the feature from feature subset according to SBS algorithm. In this way, the better feature subset can be obtained. The experiment results show that the proposed hybrid feature selection algorithm has good classification performance for liver tissue pathological image.