Artificial rabbits optimization algorithm with automatically DBSCAN clustering algorithm to similarity agent update for features selection problems

Feature selection is one of the important steps in data mining to reduce the dimensions of datasets. Due to the fact that feature selection is inherently a NP-hard problem, no deterministic algorithm has been identified to solve this problem in acceptable time. Meta-heuristic algorithms are reliable...

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Veröffentlicht in:The Journal of supercomputing Jg. 81; H. 1; S. 150
Hauptverfasser: Hamdipour, Ali, Basiri, Abdolali, Zaare, Mostafa, Mirjalili, Seyedali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.01.2025
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Classification
Clustering
Collaboration
Compilers
Computer Science
Data mining
Datasets
Feature selection
Heuristic
Heuristic methods
Interpreters
Machine learning
Methods
Neural networks
Optimization
Optimization algorithms
Performance enhancement
Performance evaluation
Processor Architectures
Programming Languages
Source code
DBSCAN
Feature selection
Artificial rabbits optimization
ARO
Optimization
ISSN:0920-8542, 1573-0484
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Zusammenfassung:Feature selection is one of the important steps in data mining to reduce the dimensions of datasets. Due to the fact that feature selection is inherently a NP-hard problem, no deterministic algorithm has been identified to solve this problem in acceptable time. Meta-heuristic algorithms are reliable alternatives to solve such problems in acceptable time. In the literature, a large number of algorithms have been proposed to solve the feature selection problem using meta-heuristic optimization algorithms. In this work, a new feature selection algorithm based on ARO meta-heuristic algorithm and DBSCAN clustering algorithm with automatic adjustment of input parameters (ARO-DBSCAN) is proposed. Using side algorithms to improve the performance of meta-heuristic algorithms can potentially cause getting stuck in local optima. The method proposed in the work has improved the performance of the ARO meta-heuristic for the feature selection problem without increasing the probability stuck in local optima. The use of DBSCAN clustering algorithm, which is based on density, increases the exploitation of ARO in the search space while maintaining its exploration. As a result, the performance of the ARO algorithm increases significantly in feature selection problems. The proposed algorithm is compared with 8 state-of-the-art feature selection algorithms on the UCI benchmark datasets and three real-world high-dimensional datasets. Result of experiments show the better performance of ARO-DBSCAN algorithm in the appropriate execution time. Also, in high-dimensional data, the proposed method is able to significantly reduce the number of dataset features. which makes the analysis of these datasets more efficient. The source code for the algorithm being proposed is accessible to the public on https://github.com/alihamdipour/ARO-DBSCAN .
Bibliographie:ObjectType-Article-1
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-024-06606-8