A Scalable Feature Selection and Opinion Miner Using Whale Optimization Algorithm

• URL: http://arxiv.org/abs/2004.13121v1
• Date: Tue, 21 Apr 2020 01:08:45 GMT
• Title: A Scalable Feature Selection and Opinion Miner Using Whale Optimization Algorithm
• Authors: Amir Javadpour, Samira Rezaei, Kuan-Ching Li and Guojun Wang
• Abstract summary: Using feature selection techniques not only support to understand data better but also lead to higher speed and also accuracy. In this article, the Whale Optimization algorithm is considered and applied to the search for the optimum subset of features.
• Score: 6.248184589339059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the fast-growing volume of text documents and reviews in recent years, current analyzing techniques are not competent enough to meet the users' needs. Using feature selection techniques not only support to understand data better but also lead to higher speed and also accuracy. In this article, the Whale Optimization algorithm is considered and applied to the search for the optimum subset of features. As known, F-measure is a metric based on precision and recall that is very popular in comparing classifiers. For the evaluation and comparison of the experimental results, PART, random tree, random forest, and RBF network classification algorithms have been applied to the different number of features. Experimental results show that the random forest has the best accuracy on 500 features. Keywords: Feature selection, Whale Optimization algorithm, Selecting optimal, Classification algorithm

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