计算机科学
特征选择
选择(遗传算法)
人工智能
基因选择
机器学习
朴素贝叶斯分类器
贝叶斯定理
模式识别(心理学)
任务(项目管理)
数据挖掘
支持向量机
基因
贝叶斯概率
微阵列分析技术
基因表达
生物化学
经济
化学
管理
作者
Rosa M. Blanco,Pedro Larrañaga,Iñaki Inza,Basilio Sierra
标识
DOI:10.1142/s0218001404003800
摘要
Despite the fact that cancer classification has considerably improved, nowadays a general method that classifies known types of cancer has not yet been developed. In this work, we propose the use of supervised classification techniques, coupled with feature subset selection algorithms, to automatically perform this classification in gene expression datasets. Due to the large number of features of gene expression datasets, the search of a highly accurate combination of features is done by means of the new Estimation of Distribution Algorithms paradigm. In order to assess the accuracy level of the proposed approach, the naïve-Bayes classification algorithm is employed in a wrapper form. Promising results are achieved, in addition to a considerable reduction in the number of genes. Stating the optimal selection of genes as a search task, an automatic and robust choice in the genes finally selected is performed, in contrast to previous works that research the same types of problems.
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