CAJ | 학술논문

已有基于支持向量数据描述的特征选择方法计算量较大，导致特征选择的时间过长。针对此问题，提出了一种新的基于支持向量数据描述的特征选择算法。新方法的特征选择是通过超球体球心方向上的能量大小来决定且采用了递归特征消除方式来逐渐剔除掉冗余特征。在Leukemia数据集上的实验结果表明，新方法能够进行快速的特征选择，且所选择的特征对后续的分类是有效的。
이유기우지지향량수거묘술적특정선택방법계산량교대，도치특정선택적시간과장。침대차문제，제출료일충신적기우지지향량수거묘술적특정선택산법。신방법적특정선택시통과초구체구심방향상적능량대소래결정차채용료체귀특정소제방식래축점척제도용여특정。재Leukemia수거집상적실험결과표명，신방법능구진행쾌속적특정선택，차소선택적특정대후속적분류시유효적。
There have been proposed feature selection methods based on support vector data description ( SVDD) , or SVDD?radius?RFE and SVDD?dual?objective?RFE. These methods are time consuming due to the high computa?tional complexity. To remedy it, a support vector data description?based feature selection method is proposed, ie SVDD?RFE. In this method, feature elimination depends on the energy of directions in the center of hypersphere. In addition, a scheme of recursive feature elimination ( RFE) is introduced to iteratively remove irrelevant features. Experimental results on the Leukemia dataset showed that this method has fast speed for feature selection, and the selected features are efficient for subsequent classification tasks.