基于类标号扩展的半监督特征选择算法

特征选择是数据挖掘、机器学习等领域的重要内容,在缺乏已标记样本的情况下,如何有效选择特征是一个非常值得研究的问题。基于集合间相关度与自相关度的定义,提出了一种新颖的半监督特征选择方法,从原始、少量、且已标记的训练样本出发,通过扩展类标号得到最终的聚类效果,采用复合的评价方法作为衡量特征子集的标准。大量实验结果表明,该算法是有效的。

Semi-supervised Feature Selection Algorithm Based on Extension of Label

Feature selection is an important step during data mining and machine learning.With the lack of labeled instances,the problem of effective selection is worthy of consideration.This paper proposed a novel semi-supervised feature selection algorithm based on the definition of inter-set and intra-set correlation,which starts from the original and small labeled samples and gains the final clusters by extension of labels.A complex evaluation was utilized as criterion to find optimal feature subset.Finally,the ex...