@inproceedings{b843fb2a14f246278efee12945323101,
title = "A Scalable Feature Selection and Opinion Miner Using Whale Optimization Algorithm",
abstract = "Due to the fast-growing volume of text document and reviews in recent years, current analyzing techniques are not competent enough to meet the users{\textquoteright} 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 = "Classification algorithm, Feature selection, Selecting optimal, Whale Optimization algorithm",
author = "Amir Javadpour and Samira Rezaei and Li, \{Kuan Ching\} and Guojun Wang",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2020.; 5th International Symposium on Signal Processing and Intelligent Recognition Systems, SIRS 2019 ; Conference date: 18-12-2019 Through 21-12-2019",
year = "2020",
doi = "10.1007/978-981-15-4828-4\_20",
language = "英语",
isbn = "9789811548277",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "237--247",
editor = "Thampi, \{Sabu M.\} and Hegde, \{Rajesh M.\} and Sri Krishnan and Jayanta Mukhopadhyay and Vipin Chaudhary and Oge Marques and Selwyn Piramuthu and Corchado, \{Juan M.\}",
booktitle = "Advances in Signal Processing and Intelligent Recognition Systems - 5th International Symposium, SIRS 2019, Revised Selected Papers",
address = "德国",
}