Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection

Currently, the masses are interested in sharing opinions, feedbacks, suggestions on any discrete topics on websites, e-forums, and blogs. Thus, the consumers tend to rely a lot on product reviews before buying any products or availing their services. However, not all reviews available over internet are authentic. Spammers manipulate the reviews in their favor to either devalue or promote products. Thus, customers are influenced to take wrong decision due to these spurious reviews, i. e., spammy contents. In order to address this problem, a hybrid approach of improved binary particle swarm optimization and shuffled frog leaping algorithm are proposed to decrease high dimensionality of the feature set and to select optimized feature subsets. Our approach helps customers in ignoring fake reviews and enhances the classification performance by providing trustworthy reviews. Naive Bayes (NB), K Nearest Neighbor (kNN) and Support Vector Machine (SVM) classifiers were used for classification. The results indicate that the proposed hybrid method of feature selection provides an optimized feature subset and obtains higher classification accuracy.