基于伪标签一致度的不平衡数据特征选择算法

Feature selection algorithm for imbalanced data based on pseudo-label consistency

针对大多数粒计算特征选择算法未考虑数据的类别不平衡性的问题,提出一种融合伪标签策略的类别不平衡数据特征选择算法。首先,为了便于研究类别不平衡数据特征选择算法,重新定义样本和数据集一致度的概念,并设计了相应特征选择的贪婪前向搜索算法;其次,引入伪标签策略以平衡数据的类别分布,并将所学样本的伪标签融入一致性测度中,以构造伪标签一致度来估计类别不平衡数据集的特征;最后,通过保持类别不平衡数据集的伪标签一致度不变,设计一种面向类别不平衡数据的基于伪标签一致性的特征选择算法(PLCFS)。实验结果表明,所提PLCFS的性能仅次于最大相关最小冗余(mRMR)算法,而优于Relief算法和基于一致性的特征选择算法(CFS)。

Aiming at the problem that most algorithms of granular computing ignore the class-imbalance of data,a feature selection algorithm integrating pseudo-label strategy was proposed to deal with class-imbalanced data.Firstly,to investigate feature selection from class-imbalanced data conveniently,the sample consistency and dataset consistency were re-defined,and the corresponding greedy forward search algorithm for feature selection was designed.Then,the pseudo-label strategy was introduced to balance the class distribution of the data.By integrating the learned pseudo-label of a sample into consistency measure,the pseudo-label consistency was defined to estimate the features of the class-imbalanced dataset.Finally,an algorithm for Pseudo-Label Consistency based Feature Selection(PLCFS)for class-imbalanced data was developed based on the preservation of the pseudo-label consistency measure for the class-imbalanced dataset.Experimental results indicate that the proposed PLCFS has the performance only lower than max-Relevancy and Min-Redundancy(mRMR)algorithm,but outperforms Relief algorithm and algorithm for Consistency-based Feature Selection(CFS).