基于改进遗传算法和LVQ神经网络包裹算法的特征筛选

在皮肤症状计算机辅助测试系统研究中，症状特征的筛选是提高系统诊断的关键问题，针对这个问题提出基于遗传算法和LVQ神经网络相结合的包裹算法。同时为了提高搜索效率，采用改进的自适应遗传算法。并用留一交叉法验证LVQ神经网络分类器的识别率．对初步提取的体现病态皮肤症状特点的22个特征以及它们的10个随意组合构成的干扰项进行特征选择，选择出使皮肤症状诊断率得到明显提高的特征组合。实验证明该方法是可行的。

Features Selection Based on MAGA/LVQ Wrapper Approach

In the research of computer-aided diagnostic(CAD) system for the skin symptom diagnosis, feature selection is a key process and affects the design and performance of the classifier. A new method based on Genetic Algorithms(GA) and LVQ Neural Networks wrapper approach is introduced to select rational feature from the originally 22 collected which has been extracted from the Skin Micro-image by image processing and 10 interferential items. At the same time, a modified adaptive genetic algorithm(MAGA) is introduced to enhance the searching efficiency. Furthermore, Leave-one-out cross-validation(LOOCV) scheme is employed to test the performance of LVQ Neural Networks classification,experimental results are satisfied.