A hybrid model using teaching–learning-based optimization and Salp swarm algorithm for feature selection and classification in digital mammography

• Published in: Journal of ambient intelligence and humanized computing Vol. 12; no. 9; pp. 8793 - 8808
• Main Author: Thawkar, Shankar
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021; Springer Nature B.V
• Subjects: Adaptive systems; Artificial Intelligence; Artificial neural networks; Breast cancer; Classification; Computational Intelligence; Engineering; Feature selection; Fuzzy logic; Genetic algorithms; Heuristic; Mammography; Mathematical functions; Neural networks; Optimization; Optimization algorithms; Original Research; Performance enhancement; Photovoltaic cells; Research methodology; Robotics and Automation; Teaching; User Interfaces and Human Computer Interaction; Salp swarm algorithm; Feature selection; Teaching learning based optimization; Classification; Mammography; Artificial neural network; Adaptive neuro-fuzzy inference system
• ISSN: 1868-5137; 1868-5145
• DOI: 10.1007/s12652-020-02662-z

Feature selection is the most important step in the design of a breast cancer diagnosis system. The basic objective of the proposed methodology is to reduce the size of the feature space to improve the performance of the classification system. In this article, a hybrid teaching–learning based optimization (TLBO) with a Salp swarm algorithm (SSA) is presented to select the features with an artificial neural network as a fitness evaluator. The features selected by TLBO-SSA are evaluated using an adaptive neuro-fuzzy inference system. The performance of the proposed methodology is tested over 651 mammograms. The experimental results show that TLBO-SSA appears to be the best when compared with the basic TLBO algorithm. TLBO-SSA archived an accuracy of 98.46% with 98.81% sensitivity, 98.08% specificity, 0.9852 F-score, 0.9692 Cohen’s kappa coefficient, and area under curve A Z  = 0.997 ± 0.001. Again the robustness of the proposed TLBO-SSA method is tested using a benchmark dataset obtained from the UCI repository. The result obtained by TLBO-SSA is compared with the Genetic Algorithm. The results show that TLBO-SSA is better than the Genetic Algorithm.