An improved ensemble pruning for mammogram classification using modified Bees algorithm

• Published in: Neural computing & applications Vol. 34; no. 12; pp. 10093 - 10116
• Main Authors: Qasem, Ashwaq, Sheikh Abdullah, Siti Norul Huda, Sahran, Shahnorbanun, Albashish, Dheeb, Goudarzi, Shidrokh, Arasaratnam, Shantini
• Format: Journal Article
• Language: English
• Published: London Springer London 01-06-2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Breast cancer; Classification; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Datasets; Diagnostic systems; Heuristic methods; Image analysis; Image Processing and Computer Vision; Lung cancer; Machine learning; Mammography; Methods; Original Article; Probability and Statistics in Computer Science; Search algorithms; Bees algorithm; Medical image processing; Ensemble fusion; Ensemble pruning
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-06995-y

Ensemble learning has piqued the curiosity of the machine learning applications. It recently drawn serious attention in computer-aided diagnostic system (CADs) due to their potential to significantly increase the prediction performance, especially in mass classification in the mammogram. The ensemble pruning technique is used to reduce the ensemble size and improve its performance by selecting an optimal subset from a pool of individual classifiers. Among ensemble pruning techniques, the metaheuristic Bees algorithm (BA) showed reliable performance in terms of the selected ensemble's accuracy. However, BA's random initialization cannot guarantee whether the optimal ensemble will be selected, which leads to lesser classification results. Thus, this study introduces a selective Random Start Best step (RSB) initialization for BA to get an optimal ensemble pruning solution. Moreover, fusing ensemble members with equal weights will reduce the performance of the ensemble. To overcome this issue, a Local Weighted Majority Voting (L-WMV) is proposed beside the RSB. The proposed RSB(L-WMV) method for solving ensemble pruning and fussing issues was assessed on the mammogram image dataset that has been collected from Hospital Kuala Lumpur (HKL). Furthermore, the proposed RSB(L-WMV) was applied to the Mammographic Image Analysis Society (MIAS) benchmark dataset to show the proposed method's effectiveness. The obtained results using various evaluation metrics (accuracy, sensitivity, specificity, AUC) reveal the superiority of the proposed RSB(L-WMV) compared to similar methods in the literature.