摘要
针对传统特征选择算法局限于单标签数据问题,提出一种多标签数据特征选择算法——多标签ReliefF算法。该算法依据多标签数据类别的共现性,假设样本各类标签的贡献值是相等的,结合三种贡献值计算方法,改进特征权值更新公式,最终获得有效的分类特征。分类实验结果表明,在特征维数相同的情况下,多标签ReliefF算法的分类正确率明显高于传统特征选择算法。
The traditional feature selection algorithms are limited to single-label data. Concerning this problem, multi- label ReliefF algorithm was proposed for multi-label feature selection. For multi-label data, based on label co-occurrence, this algorithm assumed the label contribution value was equal. Combined with three new methods calculating the label contribution, the updating formula of feature weights was improved. Finally a distinguishable feature subset was selected from original features. Classification experiments demonstrate that, with the same number of features, classification accuracy of the proposed algorithm is obviously higher than the traditional approaches.
出处
《计算机应用》
CSCD
北大核心
2012年第10期2888-2890,2898,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(61073116
61003131)
安徽省高校自然科学研究重点项目(KJ2010A006)
关键词
特征选择
多标签
RELIEFF
降维
模式识别
feature selection
multi-label
RefiefF
dimensionality reduction
pattern recognition
