Multi-objective Grey Wolf Optimizer for improved cervix lesion classification

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• 作者：Anita Sahoo^a ; ^{anitasahu1@gmail.com} ; Satish Chandra^b ; ^{satish.chandra@jiit.ac.in}
• 关键词：Cervix lesion characterization ; Classification ; Feature selection ; Grey Wolf Optimizer ; Multi-objective optimization ; Textural features ; Wavelet features ; Wrapper based feature selection
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：52
• 期：Complete
• 页码：64-80
• 全文大小：2501 K
• 卷排序：52

文摘

This is a novel effort towards effective characterization of cervix lesions from CECT images. Two different approaches have been adopted for designing multi-objective binary GWO algorithms. For the utilized cases, Non-dominated Sorting based GWO dominates the other meta-heuristics based methods compared with. Cervix lesions are up to 91.1% accurately classified as benign and malignant with only five features selected by NSGWO. Efficiency of NSGWO is further verified on high-dimensional microarray gene expression datasets available online.