摘要：In our work, we propose an ensemble of local and global filter-based
feature selection method to reduce the high dimensionality of feature space and
increase accuracy of spam review classification. These selected features are then
used for training various classifiers for spam detection. Experimental results with
four classifiers on two available datasets of hotel reviews show that the proposed
feature selector improves the performance of spam classification in terms of wellknown
performance metrics such as AUC score.
关键词：Feature selection; improved global feature selector; odds ratio; Spam;
classification.
