Adaptive feature selection using v-shaped binary particle swarm optimization

Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate th...

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Bibliographic Details
Published in:PloS one Vol. 12; no. 3; p. e0173907
Main Authors: Teng, Xuyang, Dong, Hongbin, Zhou, Xiurong
Format: Journal Article
Language:English
Published: United States Public Library of Science 30.03.2017
Public Library of Science (PLoS)
Subjects:
Adaptive algorithms
Algorithms
Analysis
Artificial intelligence
Classification
Computer and Information Sciences
Computer science
Correlation analysis
Cybernetics
Data mining
Data Mining - statistics & numerical data
Data processing
Datasets
Drafting
Engineering
Entropy
Entropy (Information theory)
Evaluation
Evolution
Expert systems
Game theory
Gene mapping
Gravity
Humans
Image processing
Information processing
Information theory
Instrumentation
Learning algorithms
Machine learning
Machine Learning - statistics & numerical data
Mathematical models
Methods
Neurocomputing
Noise
Optimization
Optimization theory
Particle swarm optimization
Pattern recognition
Physical Sciences
Physical training
Police
R&D
Redundancy
Research & development
Research and Analysis Methods
Searching
Social interactions
Social organization
Statistical analysis
Support Vector Machine - statistics & numerical data
Swarm intelligence
Technology
Transfer functions
China
ISSN:1932-6203, 1932-6203
Online Access:Get full text
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Description
Summary:Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their combined effect. Moreover, a greedy search considers only the optimization of the current round and thus cannot be a global search. To evaluate the combined effect of different subsets in the entire feature space, an adaptive feature selection method based on V-shaped binary particle swarm optimization is proposed. In this method, the fitness function is constructed using the correlation information entropy. Feature subsets are regarded as individuals in a population, and the feature space is searched using V-shaped binary particle swarm optimization. The above procedure overcomes the hard constraint on the number of features, enables the combined evaluation of each subset as a whole, and improves the search ability of conventional binary particle swarm optimization. The proposed algorithm is an adaptive method with respect to the number of feature subsets. The experimental results show the advantages of optimizing the feature subsets using the V-shaped transfer function and confirm the effectiveness and efficiency of the feature subsets obtained under different classifiers.
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Competing Interests: The authors have declared that no competing interests exist.
Conceptualization: XT.Formal analysis: HD.Investigation: XT.Methodology: HD XT.Resources: HD XZ.Visualization: HD XZ.Writing – original draft: XT.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0173907