Abstract: In order to improve the splice site recognition accuracy of Fuzzy Support Vector Machine (FSVM), a new method for computing the membership degree of sample was proposed. The initial membership was defined as the distance ratio of the sample to the two cluster centers of positive and negative samples, K-Nearest Neighbor (KNN) was adopted to compute the tightness of the samples, and the multiplication of the tightness and the initial membership degree was used as the ultimate membership. It will not only improve the membership degree of support vector, but also reduce the membership degree of noise sample. This method was applied to recognize the splice site, and the experimental results show that the recognition accuracy of constitutive 5′ and 3′ splice site reaches 94.65% and 88.97% respectively. Compared with the classical support vector machine，the recognition accuracy of constitutive 3′ splice site increases by 7.94%.

Key words: Fuzzy Support Vector Machine (FSVM), membership degree, tightness, splice site recognition, alternative splice

摘要： 为了提高模糊支持向量机(FSVM)对剪接位点的识别精度，提出一种计算样本隶属度的新方法。将样本到两聚类中心的距离比值作为样本的初始隶属度，采用K近邻(KNN)方法计算样本的紧密度，最后将初始隶属度与紧密度的乘积作为样本的最终隶属度，这样既提高了支持向量的隶属度，又降低了噪声样本的隶属度。将此方法应用到剪接位点的识别中，对组成性5′和3′剪接位点的识别精度分别达到了94.65%和 88.79%，与经典支持向量机相比，3′剪接位点的识别精度提高了7.94%。

关键词: 模糊支持向量机, 隶属度, 紧密度, 剪接位点识别, 选择性剪接