CAJ | 학술논문

当特征集合存在强相关的特征子集且共同对分类问题有重要贡献时，传统方法通常从该子集中随机选择一个特征，导致数据可读性和分类性能下降。为此，面向多分类问题，提出一种基于支持向量机的特征选择算法，并设计一种快速迭代算法。该算法能够自动选择或剔除强相关的特征子集，在得到有效特征的同时实现特征降维。利用人工数据集和标准数据集进行试验，结果表明文中算法在特征选择可行性和有效性方面都有良好表现。
당특정집합존재강상관적특정자집차공동대분류문제유중요공헌시，전통방법통상종해자집중수궤선택일개특정，도치수거가독성화분류성능하강。위차，면향다분류문제，제출일충기우지지향량궤적특정선택산법，병설계일충쾌속질대산법。해산법능구자동선택혹척제강상관적특정자집，재득도유효특정적동시실현특정강유。이용인공수거집화표준수거집진행시험，결과표명문중산법재특정선택가행성화유효성방면도유량호표현。
Most existing feature selection algorithms usually select only one feature randomly from the highly correlated feature subset with great contribution to classification,which results in the degradation of data readability and classification performance. To overcome the problem, a multi-class feature selection algorithm based on support vector machine ( MFSSVM ) is proposed. The proposed feature selection algorithm permits highly correlated features to be selected or removed together, and it allows dimension reduction while obtaining effective features. The experimental results on both simulated datasets and benchmark datasets illustrate the feasibility and effectiveness of the feature set selected by MFSSVM.