Comparison of feature selection methods for multiclass cancer classification based on microarray data
Multiclass cancer classification remains a challenging task in the field of machine learning. We presented a comparative study of seven feature selection methods and evaluated their performance by six different types of classification methods. We applied it to the four multiclass cancer datasets. We demonstrated that feature selection is critical for multiclass cancer classification performance. We also demonstrated that an appropriate combination of feature selection techniques and classification methods makes it possible to achieve excellent performance on multiclass cancer classification task. Support vector machine method based on recursive feature elimination (SVM-RFE) feature selection algorithm combined with sequential minimal optimization algorithm for training support vector machines (SMO) classification method showed the best performance.
feature selection multiclass cancer classification comparative study SVM-RFE support vector machines
Xiaobo Li Sihua Peng Xiaosi Zhan Jinxiang Zhang Yueming Xu
School of Information Science and Technology Zhejiang International Studies University Hangzhou 3100 Department of Surgery, School of Medicine Stanford University Stanford, CA 94305-5101, USA School of Information Science and Technology Zhejiang International Studies University Hangzhou 3100
国际会议
上海
英文
1704-1708
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）