基于遗传算法和决策树的肿瘤分类规则挖掘

提出了一种从肿瘤的基因表达数据挖掘肿瘤分类规则的方法. 首先用Bhattacharyya距离指标和相关性分析去除分类无关基因和冗余,然后以决策树作为分类器,用遗传算法搜索所得的特征空间,优化分类精度和分类模型的复杂度. 运行多次得到多个分类树和多组分类规则,由此构建组合树分类器在测试集数据上检验分类效果. 在结肠癌基因表达数据上的实验结果表明了分类规则挖掘方法的有效性和可用性.

Classification rules for mining tumors and normal tissues using genetic algorithms and decision trees

A new method was proposed to mine ensembles of groups of classification roles for tumor molecular classification, After removing irrelevant genes and redundancy from the original micro-array dataset, the GA was used to evolve gene subsets whose fimess is evaluated by the combination of classification accuracy and complexity of a decision tree. The ensemble classifier composed of the classification trees was developed to produce predications on unseen data. This method is assessed on the Colon cancer dataset and shows superior results in terms of classification performance and knowledge representation.