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A new standard error based artificial bee colony algorithm and its applications in feature selection
- Source :
- Journal of King Saud University - Computer and Information Sciences. 34:4554-4567
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- Feature selection is a basic task for pattern recognition and classification. It enhances the performance of the classification algorithms with the help of removing the redundant features. Thanks to eliminating irrelevant features, the computational time is decreased. Thus, intensive works have been carried out in this area. This paper proposes a new standard error-based artificial bee colony (SEABC) algorithm for the feature selection problem, which is developed by integrating standard error-based new solution search mechanisms into the original artificial bee colony algorithm. The SEABC algorithm is used for feature selection. Shannon entropy function is used to serve as the objective function of the SEABC algorithm. Thirteen datasets are used from UCI machine learning datasets. Features are selected according to Shannon conditional entropy values and then a threshold process is implemented to find their best relevant subset. Support Vector Machines (SVMs) and k-Nearest Neighbor (KNN) are used as the optimal classifiers. The proposed SEABC algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), ABC, improved ABC (I-ABC), Gbest-guided ABC (GABC), and PS-ABC algorithms. In general, it is observed that the SEABC algorithm achieves better classification results than other well-known algorithms.
- Subjects :
- Conditional entropy
General Computer Science
business.industry
Computer science
Particle swarm optimization
020206 networking & telecommunications
Pattern recognition
Feature selection
02 engineering and technology
Support vector machine
Artificial bee colony algorithm
Statistical classification
ComputingMethodologies_PATTERNRECOGNITION
Genetic algorithm
Pattern recognition (psychology)
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- ISSN :
- 13191578
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- Journal of King Saud University - Computer and Information Sciences
- Accession number :
- edsair.doi...........90f7ecde8177ec306333cd7691dd0028