Adaptive binary flower pollination algorithm for feature selection in review spam detection

Nowadays the primary factor for a customer for making a decision on purchasing a product is Online Reviews. Its impact is crucial since the manufacturers and retailers are highly concerned with customers feedback and reviews. Reliance on these online reviews gives rise to the potential concern that spammers may create false reviews to artificially promote or devalue products and services. This practice is known as Review Spam. Feature selection is significant aspect for classification. This paper presents an algorithm to extract features using Adaptive Binary Flower Pollination Algorithm (BFPA) a global optimization technique. Naive Bayes classifier (NB) accuracy is used as an objective function. The experimental results from the proposed method selects only the informative features set compared to the other competitive methods and gives higher classification accuracy.