The Hybrid Feature Selection Algorithm Based on Maximum Minimum Backward Selection Search Strategy for Liver Tissue Pathological Image Classification
Joint Authors
Liu, Huiling
Zheng, Ruiping
Jiang, Huiyan
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-31
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Huiling& Jiang, Huiyan& Zheng, Ruiping. 2016. The Hybrid Feature Selection Algorithm Based on Maximum Minimum Backward Selection Search Strategy for Liver Tissue Pathological Image Classification. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1100188
Modern Language Association (MLA)
Liu, Huiling…[et al.]. The Hybrid Feature Selection Algorithm Based on Maximum Minimum Backward Selection Search Strategy for Liver Tissue Pathological Image Classification. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1100188
American Medical Association (AMA)
Liu, Huiling& Jiang, Huiyan& Zheng, Ruiping. The Hybrid Feature Selection Algorithm Based on Maximum Minimum Backward Selection Search Strategy for Liver Tissue Pathological Image Classification. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1100188
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100188