Binary Peacock Algorithm: A Novel Metaheuristic Approach for Feature Selection

Binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock algorithm (PA) for feature selection. PA, a recent metaheuristic algorithm, is built upon lekking and mating behaviors of peacocks and peahens. While d...

Full description

Saved in:
Bibliographic Details
Published in:Journal of classification Vol. 41; no. 2; pp. 216 - 244
Main Authors: Banati, Hema, Sharma, Richa, Yadav, Asha
Format: Journal Article
Language:English
Published: New York Springer US 01.07.2024
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Bioinformatics
Classification
Datasets
Experiments
Feature selection
Heuristic methods
Marketing
Mathematics and Statistics
Mating
Optimization
Pattern Recognition
Psychometrics
Random variables
Rank tests
Signal,Image and Speech Processing
Statistical Theory and Methods
Statistics
Support vector machines
Variants
Binary metaheuristic algorithms
Feature selection
Optimization
Binary peacock algorithm
Classification
ISSN:0176-4268, 1432-1343
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Binary metaheuristic algorithms prove to be invaluable for solving binary optimization problems. This paper proposes a binary variant of the peacock algorithm (PA) for feature selection. PA, a recent metaheuristic algorithm, is built upon lekking and mating behaviors of peacocks and peahens. While designing the binary variant, two major shortcomings of PA (lek formation and offspring generation) were identified and addressed. Eight binary variants of PA are also proposed and compared over mean fitness to identify the best variant, called binary peacock algorithm (bPA). To validate bPA’s performance experiments are conducted using 34 benchmark datasets and results are compared with eight well-known binary metaheuristic algorithms. The results show that bPA classifies 30 datasets with highest accuracy and extracts minimum features in 32 datasets, achieving up to 99.80% reduction in the feature subset size in the dataset with maximum features. bPA attained rank 1 in Friedman rank test over all parameters.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0176-4268
1432-1343
DOI:10.1007/s00357-024-09468-0