一种多标记数据的过滤式特征选择框架

摘要

提出一种过滤式的多标记数据特征选择框架，并在卡方检验基础上进行实现和实验研究。该框架计算每个特征在各个类标上的卡方检验，然后通过得分的统计值计算出每个特征的最终排序情况，选取了最大、平均、最小3种统计值分别进行了实验比较。在5个评价指标4、个常用的多标记数据集和3个学习器上的对比实验表明，3种得分统计方式各有优劣，但都能提高多标记学习的效果。%The researchers of multi-label learning mainly focus on the classifier performance , regardless of the influ-ence of the dataset feature .This paper proposes a filter framework of the multi-labeled data feature selection .The al-gorithm implementation and experiment were carried out based on the Chi-square test .This framework calculates the CHI-square test for each feature on each label , and then the ranking order of each feature is computed by the statis-tics of the score.This paper considers three different types of statistical data (average, maximum, minimum) for the experimental comparisons .The contrasting experiments with the four common multi-label datasets with three classifiers and five evaluation criteria show that these three score statistical methods share both superior and inferior characteristics, but still improve the performance for multi-label learning problems.