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An improved ensemble pruning for mammogram classification using modified Bees algorithm

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Abstract

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.

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Acknowledgements

We would like to thank the Ministry of Higher Education, Malaysia for supporting this project through the Fundamental Research Grant Scheme FRGS/1/2019/ICT02/UKM/02/9 entitled "Convolution neural network enhancement based on adaptive convexity and regularization functions for fake video analytics" and FRGS/1/2016/ICT02/UKM/02/10 entitle of “Commute-time convolution kernels for graph clustering to represent images. We have obtained Ethics approval with the number "UKM 1.5.3.5/244/FTSM-002-2015" entitled "Diagnostic services nexus for breast cancer" from UKM Medical Center, Malaysia, for collecting and conducting experiments on breast cancer patient records. Here, we would like and acknowledge Dr. Rizuana Iqbal Hussain from the Department of Radiology, UKM Medical Centre, Prof Fuad Ismail from Department of Radiotherapy and Oncology, UKM Medical Centre and Prof Norlia Abdullah from Department of Surgery, UKM Medical Centre for their guidance in preparing and applying ethical proposal. Finally, we got ethical approval from the Medical Research and Ethics Committee to entitle of "Diagnostic services nexus for breast cancer" with registration No "NMRR-15-21800-27949(IRR)" to allow us to collect mammogram data from HKL Finally, we would also like to thank the Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, for providing the facilities and financial support under "ETP-2013-053 diagnostic services nexus for breast cancer". We would also like to acknowledge Human Life Advancement Foundation (HLAF) for financial support to the principal researcher.

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Authors and Affiliations

  1. Faculty Information Science and Technology, Universiti Kebangsaan Malaysia (UKM), 43600, Bangi, Selangor, Malaysia

    Ashwaq Qasem, Siti Norul Huda Sheikh Abdullah, Shahnorbanun Sahran & Shidrokh Goudarzi

  2. Faculty of Engineering and Information Technology, Taiz University, Taiz, Yemen

    Ashwaq Qasem

  3. Computer Science Department, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Salt, Jordan

    Dheeb Albashish

  4. Department of Radiology, General Hospital of Kuala Lumpur, Kuala Lumpur, Malaysia

    Shantini Arasaratnam

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  1. Ashwaq Qasem
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Qasem, A., Sheikh Abdullah, S.N.H., Sahran, S. et al. An improved ensemble pruning for mammogram classification using modified Bees algorithm. Neural Comput & Applic 34, 10093–10116 (2022). https://doi.org/10.1007/s00521-022-06995-y

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