CAJ | 학술논문

基于 SVM(支持向量机)的 SVDD(支持向量数据描述)分类算法存在计算复杂、分类准确率较低的缺陷，针对股票数据非线性、高噪声的特点，在传统的 SVDD 分类算法基础上，模糊核超球快速分类算法(FCABFKH)通过合并法寻找超球集，并依据最大隶属度原则构建分类器，排除了离群点和超球集的重叠问题，同时避免了复杂的二次规划，具有分类速度快，分类结果准确率高的特点。采用中国沪市上市公司数据验证该方法的有效性，实验结果表明，运用FCABFKH算法得到的组合回报率超过了市场基准。
기우 SVM(지지향량궤)적 SVDD(지지향량수거묘술)분류산법존재계산복잡、분류준학솔교저적결함，침대고표수거비선성、고조성적특점，재전통적 SVDD 분류산법기출상，모호핵초구쾌속분류산법(FCABFKH)통과합병법심조초구집，병의거최대대속도원칙구건분류기，배제료리군점화초구집적중첩문제，동시피면료복잡적이차규화，구유분류속도쾌，분류결과준학솔고적특점。채용중국호시상시공사수거험증해방법적유효성，실험결과표명，운용FCABFKH산법득도적조합회보솔초과료시장기준。
SVDD classification algorithm based on SVM has defects, such as high calculation complexity property and low accuracy. According to nonlinear and high-noise characteristics of stock data, inspired from the idea of traditional SVDD classification algorithm, the proposed algorithm (FCABFKH) adopts mergence method to find hypersphere sets and maximum membership degree law to construct classifier. By this means, the algorithm can rule out off-group points and hypersphere sets overlap problem. Furthermore, it can avoid complex quadratic programming. Consequently, FCABFKH provides faster rate and higer accuracy. Using the data of listed companies of China A stocks market, experiments are done to test the validity of the method mentioned above. The result indicates that portfolio's return rate using classification method of FCABFKH is higher than the market benchmark.