期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2015
卷号：12
期号：5
出版社：IJCSI Press
摘要：Cancer is a group of diseases distinguished by unregulated growth and spread of cells which has become one of the most dangerous diseases. As a result of the victims of cancer are increasing steadily, the necessity is increasing to find classification techniques for cancer diseases. The present study is aimed to obtain better results of the classification model with high accuracy. Herein, we proposed a method of developing an efficient classifier based on microarray datasets. Moreover, we focused on accuracy, dimensionality reduction and fast classification issues. The proposed method Clustering ANOVA Extreme Learning Machine (CAELM) is a hybrid approach based on Extreme Leaning Machine with RBF kernel function. This hybrid approach consist of two phases: data preprocessing (normalization and genes selection) and data classifying. K-mean clustering was utilized as a method for clustering microarray datasets into three groups, then ANOVA were applied to analysis of variance between this groups to pick out the significant genes which were used in classification process. In case combining clustering with statistical analysis (CAELM) a much better classification accuracy is given of 95,94,100% for leukemia ,prostate and ovarian respectively . In addition, the proposed approach reduced time complexity with good performance.
关键词：CAELM; RBF kernel; K;mean; ANOVA; microarray; genes selection; cancer classification.
