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A novel feature selection algorithm based on damping oscillation theory
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 8, p e0255307 (2021)
- Publication Year :
- 2021
-
Abstract
- Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.
- Subjects :
- Big Data
Support Vector Machine
Computer science
Kernel Functions
Predation
Machine Learning
Redundancy (information theory)
Operator Theory
Mammals
Multidisciplinary
Ecology
Applied Mathematics
Simulation and Modeling
Physics
Eukaryota
Classical Mechanics
Trophic Interactions
Community Ecology
Feature (computer vision)
Physical Sciences
Vertebrates
Medicine
Algorithm
Algorithms
Research Article
Optimization
Computer and Information Sciences
Science
Feature selection
Research and Analysis Methods
Vibration
Machine Learning Algorithms
Position (vector)
Artificial Intelligence
Support Vector Machines
Animals
Selection algorithm
Electronic Data Processing
Wolves
Ecology and Environmental Sciences
Organisms
Biology and Life Sciences
Support vector machine
Euclidean distance
Amniotes
Noise (video)
Zoology
Mathematics
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....673e5433c58e135efe1bac85ed0ff0df