International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 181 - Number 9 |
Year of Publication: 2018 |
Authors: Mohammad Masoud Javidi, Nasibeh Emami |
10.5120/ijca2018917182 |
Mohammad Masoud Javidi, Nasibeh Emami . Proposing a New Method to Improve Feature Selection with Meta-Heuristic Algorithm and Chaos Theory. International Journal of Computer Applications. 181, 9 ( Aug 2018), 1-9. DOI=10.5120/ijca2018917182
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.