An Optimization-based Learning Black Widow Optimization Algorithm for Text Psychology

Subject Areas : Evolutionary Computing

Ali Hosseinalipour ¹ (Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN)
Farhad Soleimanian Gharehchopogh ² (Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, IRAN)
mohammad masdari ³ (Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.)
ALi Khademi ⁴ (Department of Psychology Science, Urmia Branch, Islamic Azad University, Urmia, IRAN.)

Received: 2021-01-03 Accepted : 2021-09-18 Published : 2021-02-01

Keywords:

References:

1.    Gharehchopogh, F.S. and H. Gholizadeh, A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 2019. 48: p. 1-24.
2.    Gharehchopogh, F.S., I. Maleki, and Z.A. Dizaji, Chaotic vortex search algorithm: metaheuristic algorithm for feature selection. Evolutionary Intelligence, 2021: p. 1-32.
3.    Abdollahzadeh, B. and F.S. Gharehchopogh, A multi-objective optimization algorithm for feature selection problems. Engineering with Computers, 2021: p. 1-19.
4.    Jafari, N. and F. Soleimanian Gharehchopogh, An Improved Bat Algorithm with Grey Wolf Optimizer for Solving Continuous Optimization Problems. Journal of Advances in Computer Engineering and Technology, 2020. 6(3): p. 119-130.
5.    Mohmmadzadeh, H. and F.S. Gharehchopogh, An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems. The Journal of Supercomputing, 2021: p. 1-43.
6.    Rahnema, N. and F.S. Gharehchopogh, An improved artificial bee colony algorithm based on whale optimization algorithm for data clustering. Multimedia Tools and Applications, 2020. 79(43): p. 32169-32194.
7.    Hosseinalipour, A., et al., A novel binary farmland fertility algorithm for feature selection in analysis of the text psychology. Applied Intelligence: p. 1-36.
8.    Sayed, S.A.-F., E. Nabil, and A. Badr, A binary clonal flower pollination algorithm for feature selection. Pattern Recognition Letters, 2016. 77: p. 21-27.
9.    Gharehchopogh, F.S., H. Shayanfar, and H. Gholizadeh, A comprehensive survey on symbiotic organisms search algorithms. Artificial Intelligence Review, 2019: p. 1-48.
10.    Zorarpacı, E. and S.A. Özel, A hybrid approach of differential evolution and artificial bee colony for feature selection. Expert Systems with Applications, 2016. 62: p. 91-103.
11.    Hosseinalipour, A., et al., Toward text psychology analysis using social spider optimization algorithm. Concurrency and Computation: Practice and Experience. n/a(n/a): p. e6325.
12.    Dong, H., et al., A novel hybrid genetic algorithm with granular information for feature selection and optimization. Applied Soft Computing, 2018. 65: p. 33-46.
13.    Liu, B. and L. Zhang, A survey of opinion mining and sentiment analysis, in Mining text data. 2012, Springer. p. 415-463.
14.    Nasukawa, T. and J. Yi. Sentiment analysis: Capturing favorability using natural language processing. in Proceedings of the 2nd international conference on Knowledge capture. 2003.
15.    Asghar, M.Z., et al., A review of feature extraction in sentiment analysis. Journal of Basic and Applied Scientific Research, 2014. 4(3): p. 181-186.
16.    Saeys, Y., I. Inza, and P. Larrañaga, A review of feature selection techniques in bioinformatics. bioinformatics, 2007. 23(19): p. 2507-2517.
17.    Sharma, M. and P. Kaur, A Comprehensive Analysis of Nature-Inspired Meta-Heuristic Techniques for Feature Selection Problem. Archives of Computational Methods in Engineering, 2020: p. 1-25.
18.    Emine, B. and E. Ülker, An efficient binary social spider algorithm for feature selection problem. Expert Systems with Applications, 2020. 146: p. 113185.
19.    Hayyolalam, V. and A.A.P. Kazem, BWO algorithm: A novel meta-heuristic approach for solving engineering optimization problems. Engineering Applications of Artificial Intelligence, 2020. 87: p. 103249.
20.    Pang, B., L. Lee, and S. Vaithyanathan, Thumbs up? Sentiment classification using machine learning techniques. arXiv preprint cs/0205070, 2002.
21.    Arora, S. and P. Anand, Binary butterfly optimization approaches for feature selection. Expert Systems with Applications, 2019. 116: p. 147-160.
22.    Hussien, A.G., et al., S-shaped binary whale optimization algorithm for feature selection, in Recent trends in signal and image processing. 2019, Springer. p. 79-87.
23.    Bennasar, M., Y. Hicks, and R. Setchi, Feature selection using joint mutual information maximisation. Expert Systems with Applications, 2015. 42(22): p. 8520-8532.
24.    Mirjalili, S., Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 2016. 27(4): p. 1053-1073.
25.    Yang, X.S. and A.H. Gandomi, Bat algorithm: a novel approach for global engineering optimization. Engineering computations, 2012.
26.    Leskovec, J., A. Rajaraman, and J.D. Ullman, Mining of massive data sets. 2020: Cambridge university press.
27.    Mafarja, M.M. and S. Mirjalili, Hybrid whale optimization algorithm with simulated annealing for feature selection. Neurocomputing, 2017. 260: p. 302-312.
28.    Liao, T.W. and R. Kuo, Five discrete symbiotic organisms search algorithms for simultaneous optimization of feature subset and neighborhood size of knn classification models. Applied Soft Computing, 2018. 64: p. 581-595.
29.    Mafarja, M., et al., Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems. Knowledge-Based Systems, 2018. 145: p. 25-45.
30.    Rajamohana, S. and K. Umamaheswari, Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection. Computers & Electrical Engineering, 2018. 67: p. 497-508.
31.    Azar, A.T., et al., A random forest classifier for lymph diseases. Computer methods and programs in biomedicine, 2014. 113(2): p. 465-473.