Intelligent Feature Selection Methods: A Survey | ||
| Engineering and Technology Journal | ||
| Article 17, Volume 39, 1B, March 2021, Pages 175-183 PDF (326.68 K) | ||
| DOI: 10.30684/etj.v39i1B.1623 | ||
| Authors | ||
| Noor Jameel* 1; Hasanen S. Abdullah2 | ||
| 1Department of Computer Science, University of Technology, Baghdad, Iraq, 110443@uotechnology.edu.iq | ||
| 2Department of Computer Science, University of Technology, Baghdad, Iraq, 110014@uotechnology.edu.iq | ||
| Abstract | ||
| Consider feature selection is the main in intelligent algorithms and machine learning to select the subset of data to help acquire the optimal solution. Feature selection used an extract the relevance of the data and discarding the irrelevance of the data with increment fast to select it and to reduce the dimensional of dataset. In the past, it used traditional methods, but these methods are slow of fast and accuracy. In modern times, however, it uses the intelligent methods, Genetic algorithm and swarm optimization methods Ant colony, Bees colony, Cuckoo search, Particle optimization, fish algorithm, cat algorithm, Genetic algorithm ...etc. In feature selection because to increment fast, high accuracy and easy to use of user. In this paper survey it used the Some the swarm intelligent method: Ant colony, Bees colony, Cuckoo search, Particle optimization and Genetic algorithm (GA). It done take the related work for each algorithms the swarm intelligent the ideas, dataset and accuracy of the results after that was done to compare the result in the table among the algorithms and learning the better algorithm is discuses in the discussion and why it is better. Finally, it learning who is the advantage and disadvantage for each algorithms of swarm intelligent in feature selection. | ||
| Keywords | ||
| Feature selection; Intelligent Feature Selection Survey; Intelligent Applications. Swarm intelligence | ||
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