基于属性约简和支持向量机集成的乳腺癌诊断决策

针对遗传算法(GA)与支持向量机(SVM)集成相结合的疾病诊断方法存在属性冗余的问题,提出了一种改进的约简和诊断乳腺癌决策方法。该方法将最小化约简属性个数、最大化区分矩阵可区别属性的个数以及最大化约简属性对决策属性的依赖度这三种目标函数相结合作为GA的适应度函数。在约简属性后取多个子集,以便利用SVM集成学习。在UCI数据库中乳腺癌数据集的实验表明,与原始的SVM算法相比,该方法在分类诊断的准确度以及敏感性方面有一定的提高,其中分类准确度至少提高了2%。

Diagnosis decision of breast cancer combining with attribute reduction and support vector machine

In the disease diagnosis approach of combining with Gene Algorithm (GA) and Support Vector Machine (SVM) ensemble, the attribute redundancy problem still exists. A decision method for diagnosis of breast cancer was proposed based on attribute reduction and SVM. The proposed attribute reduction method took minimizing the attribute number, maximizing the difference attribute number in discernibility matrix and maximizing the dependency degree of condition reduction attributes on decision attributes as the fitness function of GA. After attribute reduction, multiple attribute subsets were selected for SVM ensemble learning. Compared with SVM, experimental results on the breast cancer dataset from UCI databases validate that the classification accuracy increases by 2 percent at least.