Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms

Abdelaziz A. Abdelhamid, El Sayed M. El-kenawy, Abdelhameed Ibrahim, Marwa M. Eid, Doaa Sami Khafaga, Amel Ali Alhussan, Seyedali Mirjalili, Nima Khodadadi, Wei Hong Lim, Mahmoud Y. Shams

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


<italic>Introduction</italic>: 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. <italic>Methodology</italic>: 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&#x2019; effectiveness in classification problems. <italic>Results</italic>: The achieved results demonstrate the algorithm&#x2019;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&#x2019;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. <italic>Conclusion</italic>: These results emphasized the proposed feature selection method&#x2019;s significance, superiority, and statistical difference.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Access
Publication statusPublished - 2023


  • Classification algorithms
  • Dipper throated optimization algorithm
  • Feature extraction
  • Feature selection
  • Genetic algorithms
  • Machine learning
  • Mathematical models
  • Meta-heuristic optimization
  • Metaheuristics
  • Optimization
  • Sine cosine optimization algorithm


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