CAJ | 학술논문

在分析已有不完备信息熵的基础上，提出一种基于相似关系的不完备信息熵，并证明该信息熵的若干性质。给出一个不完备数据特征选择算法，算法以改进的不完备信息熵作为特征选择准则，直接对不完备数据的特征进行熵值分析，并采用顺序前向浮动选择方法解决特征间的相关性问题。最后在UCI实测数据集上的实验表明，文中算法具有更高的准确率和更快的特征选择速度。
재분석이유불완비신식적적기출상，제출일충기우상사관계적불완비신식적，병증명해신식적적약간성질。급출일개불완비수거특정선택산법，산법이개진적불완비신식적작위특정선택준칙，직접대불완비수거적특정진행적치분석，병채용순서전향부동선택방법해결특정간적상관성문제。최후재UCI실측수거집상적실험표명，문중산법구유경고적준학솔화경쾌적특정선택속도。
Grounded on the analysis of the existing incomplete information entropy, the concept of incomplete information entropy based on similarity relations ( SIIE ) is proposed, and some properties of SIIE are discussed. A feature selection algorithm for incomplete data is presented. In this algorithm, SIIE of incomplete data is calculated directly, and SIIE is taken as the criteria for feature selection. Then, the sequential forward floating search method is employed to addresses the problem of correlation among features. Experiments on UCI database are carried out, and the results indicate the accuracy and efficiency of the proposed algorithm.