计算机科学与探索2017，Vol.11Issue(10)：1570-1578,9.DOI:10.3778/j.issn.1673-9418.1608046
面向基因数据分类的核主成分分析旋转森林算法
Classifier Algorithm of Genetic Data Based on Kernel Principal Component Anal-ysis and Rotation Forest
摘要
Abstract
Rotation forest (RoF) algorithm is an ensemble classification algorithm using linear analysis theory and decision trees. The rotation forest achieves higher classification accuracy and superior performance with less num-ber of classifiers. However, the classification accuracy decreases for gene expression data with linearly inseparable cases. To address this issue, this paper proposes a rotation forest algorithm based on kernel principal component analysis (KPCA-RoF), chooses the Gaussian kernel function and principal component analysis to implement the nonlinear mapping and deal with differences in gene data. The proposed algorithm focuses on the optimization of parameters, and uses decision tree algorithm for ensemble learning. Experiments show that the new algorithm well addresses the linearly inseparabal issue and improves the classification accuracy.关键词
核函数/主成分分析/决策树/旋转森林/基因数据分类Key words
kernel function/principal component analysis/decision tree/rotation forest/gene data classification分类
信息技术与安全科学引用本文复制引用
陆慧娟,刘亚卿,孟亚琼,关伟,刘砚秋..面向基因数据分类的核主成分分析旋转森林算法[J].计算机科学与探索,2017,11(10):1570-1578,9.基金项目
The National Natural Science Foundation of China under Grant Nos. 61272315, 60905034 (国家自然科学基金) （国家自然科学基金）
the Natural Science Foundation of Zhejiang Province under Grant No. Y1110342 (浙江省自然科学基金) （浙江省自然科学基金）
the National Security Bureau Project under Grant No. zhejiang-00062014AQ (国家安全总局项目). （国家安全总局项目）
