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Proposing a New Method to Improve Feature Selection with Meta-Heuristic Algorithm and Chaos Theory

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Mohammad Masoud Javidi, Nasibeh Emami

Mohammad Masoud Javidi and Nasibeh Emami. Proposing a New Method to Improve Feature Selection with Meta-Heuristic Algorithm and Chaos Theory. International Journal of Computer Applications 181(9):1-9, August 2018. BibTeX

	author = {Mohammad Masoud Javidi and Nasibeh Emami},
	title = {Proposing a New Method to Improve Feature Selection with Meta-Heuristic Algorithm and Chaos Theory},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2018},
	volume = {181},
	number = {9},
	month = {Aug},
	year = {2018},
	issn = {0975-8887},
	pages = {1-9},
	numpages = {9},
	url = {},
	doi = {10.5120/ijca2018917182},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Finding a subset of features from a large data set is a problem that arises in many fields of study. It is important to have an effective subset of features that is selected for the system to provide acceptable performance. This will lead us in a direction that to use meta-heuristic algorithms to find the optimal subset of features. The performance of evolutionary algorithms is dependent on many parameters which have significant impact on its performance, and these algorithms usually use a random process to set parameters. The nature of chaos is apparently random and unpredictable; however it also deterministic, it can suitable alternative instead of random process in meta-heuristic algorithms.


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Feature selection, Classification, Meta-heuristic algorithm, Binary particle swarm optimization, Chaos theory.