Open AccessA new method to improve feature selection with meta-heuristic algorithm and chaos theory
Author(s) -
Mohammad Masoud Javidi,
Nasibeh Emami
Publication year - 2018
Publication title -
computer engineering and applications journal
Language(s) - English
Resource type - Journals
eISSN - 2252-5459
pISSN - 2252-4274
DOI - 10.18495/comengapp.v7i1.225
Subject(s) - heuristic , computer science , algorithm , set (abstract data type) , chaos (operating system) , feature (linguistics) , meta heuristic , process (computing) , feature selection , evolutionary algorithm , selection (genetic algorithm) , mathematical optimization , artificial intelligence , mathematics , linguistics , philosophy , computer security , programming language , operating system
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