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Optimizing the Selection of Base Learners for Multiple Classifier System in Liver Cancer Identification Using Contribution-based Iterative Removal Algorithm

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Abstract

In the healthcare industry, developing an efficient diagnostic system to classify liver cancer cells is a very perplexing and arduous task. Recently, several studies demonstrate that deep ensemble classifiers can achieve better predictive accuracy than individual deep learning classifiers. The deep ensemble learners exploit more than one individual deep learner to achieve better classification results and improved generalization performance. When implementing an ensemble learning (multiple classifier) approach, the selection of the optimum learners from a crew is a critical issue and an effective learner assortment strategy is used to achieve better results. Several researchers have applied different approaches (e.g., rule-based algorithms, evolutionary computing, simulated annealing, etc.) to determine the optimal learners that can increase the performance of the diagnostic system. This work proposes a new classifier selection strategy to construct an ensemble called a contribution-based iterative base learner removal algorithm (CIBRA). The proposed algorithm finds out the best subset of individual learners by considering both prediction accuracy and diversity. The proposed CIBRA enables each base learner in a pool to have multiple chances to partake in an iteration of selection. CIBRA drops the classifiers only if they have no residual opportunities. This procedure is reiterated till no learner in the crew has any remaining possibility to partake in the selection round. In this study, we test various decision synthesis techniques to increase the performance of the ensemble classifier. To assess the performance of CIBRA, 8 standard cancer databases are exploited. Extensive simulation results divulge that two base classifiers are enough to classify liver cancer cells from hematoxylin and eosin (H&E) scans successfully. Based on the results obtained from this study, we construct an ensemble classifier using Dropout Extreme Learning Machine (DrpXLM) and Enhanced Convolutional Block Attention Modules (ECBAM) based residual network to classify liver cancer images. Besides, CIBRA generates better results when it operates with average probability as the decision synthesis technique.

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Data availability

Datasets for this research are retrieved from https://www.cancerimagingarchive.net/.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

    P. Sabitha & G. Meeragandhi

  2. SRM Institute of Science & Technology, Ramapuram, Chennai, Tamil Nadu, India

    P. Sabitha

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  1. P. Sabitha
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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Sabitha, P., Meeragandhi, G. Optimizing the Selection of Base Learners for Multiple Classifier System in Liver Cancer Identification Using Contribution-based Iterative Removal Algorithm. SN COMPUT. SCI. 4, 493 (2023). https://doi.org/10.1007/s42979-023-01936-5

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