article · IEEE Access
Feature selection is a critical step in pattern recognition and data mining, aimed at improving classification performance by identifying the most relevant data attributes. To address this optimisation challenge, a new hybrid binary meta-heuristic algorithm combines Dipper Throated Optimisation with the Sine Cosine algorithm. Designated as bSCWDTO, the method integrates the Sine Cosine approach to enhance exploration and achieve faster, more accurate convergence. The algorithm was evaluated across thirty benchmark datasets from the University of California Irvine repository, using a K-Nearest Neighbour classifier to assess feature quality. Testing against ten established optimisation algorithms, including Particle Swarm Optimisation and Genetic Algorithms, demonstrated that the hybrid method achieved superior performance. Statistical evaluation via Wilcoxon rank-sum testing confirmed the significant performance differences between this approach and alternative methods.
Data mining and machine learning models often struggle when handling datasets containing redundant or irrelevant variables. Improving feature selection techniques helps classifiers focus exclusively on informative data, which enhances classification efficacy. Developing more reliable optimisation algorithms provides a stronger technical foundation for pattern recognition tasks across various data-driven fields.
The abstract demonstrates early-stage algorithmic research evaluated exclusively on standard benchmark datasets. While the method could eventually assist software developers or data practitioners who need to select features for classification models, the abstract focuses on technical benchmarking and does not indicate a specific commercial application, industry partner, or commercialisation pathway.
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Introduction: In pattern recognition and data mining, feature selection is one of the most crucial tasks. To increase the efficacy of classification algorithms, it is necessary to identify the most relevant subset of features in a given domain. This means that the feature selection challenge can be seen as an optimization problem, and thus meta-heuristic techniques can be utilized to find a solution. Methodology: In this work, we propose a novel hybrid binary meta-heuristic algorithm to solve the feature selection problem by combining two algorithms: Dipper Throated Optimization (DTO) and Sine Cosine (SC) algorithm. The new algorithm is referred to as bSCWDTO. We employed the sine cosine algorithm to improve the exploration process and ensure the optimization algorithm converges quickly and accurately. Thirty datasets from the University of California Irvine (UCI) machine learning repository are used to evaluate the robustness and stability of the proposed bSCWDTO algorithm. In addition, the K-Nearest Neighbor (KNN) classifier is used to measure the selected features’ effectiveness in classification problems. Results: The achieved results demonstrate the algorithm’s superiority over ten state-of-the-art optimization methods, including the original DTO and SC, Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO), Multiverse Optimization (MVO), Satin Bowerbird Optimizer (SBO), Genetic Algorithm (GA), the hybrid of GWO and GA, and Firefly Algorithm (FA). Moreover, Wilcoxon’s rank-sum test was performed at the 0.05 significance level to study the statistical difference between the proposed method and the alternative feature selection methods. Conclusion: These results emphasized the proposed feature selection method’s significance, superiority, and statistical difference.
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DOI: 10.1109/access.2023.3298955
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