Information gain and wheel based simplified swarm optimization for gene selection from gene expression data

• Main Authors: Chang, Chung Yi, 張中議
• Other Authors: Yeh, Wei Chang
• Format: Others
• Language: en_US
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/81564909715943106056

碩士 === 國立清華大學 === 工業工程與工程管理學系 === 103 === Recently, feature selection has been an important issue in data mining problems. The object of feature selection is to find the most distinguished features among datasets which have enormous number of features and then improve the classification accuracy. Feature selection can reduce the noise and save lots of time and costs for researchers, especially when the volume of data is huge. Feature selection has wide applications for high dimensional real world situations such as cancer research in medical field. When feature selection is being used in cancer research to find cancerous genes, it is called “gene selection”. With gene selection, doctors can find the symptoms or signs of cancer at early stage and enhance the survival rate. In this paper, we try to develop an effective gene selection model for ten benchmark gene expression datasets. We proposed an information gain and wheel-based simplified swarm optimization (IG-WSSO) to solve the problem. Initially, we used information gain (IG) to remove irrelevant genes. Then, we conducted simplified swarm optimization with the wheel based search strategy for gene selection (WSSO). Support vector machine (SVM) with leave one out cross validation (LOOCV) was adopted to evaluate the accuracy. We compared our algorithm, IG-WSSO, with previous research by running ten benchmark datasets of gene expression data, which can be downloaded on: http://www.gems-system.org/. The results show IG-WSSO can achieve higher classification accuracy by selecting less number of genes.