基于模糊支持向量机的色素皮损症状识别

Recognition of pigmented skin lesions based on fuzzy support vector machines

传统支持向量机由两类扩展到多类问题时,会出现不可分区域。针对这种情况,在传统支持向量机中引入模糊隶属度函数,用模糊支持向量机(FSVM)解决了传统支持向量机在多类识别中的盲区问题。实验表明,该方法在进行皮肤色素斑症状的识别过程中效率较传统支持向量机明显提高。

Unclassifiable regions exist in conventional SVM classification with the two-class problems being extended to the multi-class problems. In order to solve this problem, fuzzy membership function was introduced in conventional SVM. Fuzzy support vector machine was applied to solve the unclassifiable problem existing in multi-class recognition by conventional SVM. The experimental results demonstrate that the proposed method outperforms the conventional SVM in the recognition of pigmented skin lesions.