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      Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer

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          Abstract

          Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.

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          SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

          Recently, support vector machine (SVM) has excellent performance on classification and prediction and is widely used on disease diagnosis or medical assistance. However, SVM only functions well on two-group classification problems. This study combines feature selection and SVM recursive feature elimination (SVM-RFE) to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Dermatology dataset contains 33 feature variables, 1 class variable, and 366 testing instances; and the Zoo dataset contains 16 feature variables, 1 class variable, and 101 testing instances. The feature variables in the two datasets were sorted in descending order by explanatory power, and different feature sets were selected by SVM-RFE to explore classification accuracy. Meanwhile, Taguchi method was jointly combined with SVM classifier in order to optimize parameters C and γ to increase classification accuracy for multiclass classification. The experimental results show that the classification accuracy can be more than 95% after SVM-RFE feature selection and Taguchi parameter optimization for Dermatology and Zoo databases.
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            Particle swarm optimisation for feature selection in classification: Novel initialisation and updating mechanisms

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              Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms

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                Author and article information

                Contributors
                Journal
                PeerJ Comput Sci
                PeerJ Comput Sci
                peerj-cs
                peerj-cs
                PeerJ Computer Science
                PeerJ Inc. (San Diego, USA )
                2376-5992
                25 January 2021
                2021
                : 7
                : e344
                Affiliations
                [1 ]Research Center for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia , 43600 Bangi, Selangor, Malaysia
                [2 ]Computer Science Department, Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University , Salt, Jordan
                Article
                cs-344
                10.7717/peerj-cs.344
                7924699
                9fdd7cbb-b913-4da6-adc2-072d93277160
                ©2021 Rahman et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.

                History
                : 1 September 2020
                : 30 November 2020
                Funding
                Funded by: The Universiti Kebangsaan Malaysia (UKM)
                Award ID: GGP-2019-023
                This research was supported by a grant from the Universiti Kebangsaan Malaysia (UKM), UKM Grant Code: GGP-2019-023). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Computational Biology
                Artificial Intelligence
                Computer Aided Design
                Data Mining and Machine Learning

                artificial neural network,breast cancer classification,hidden layer,taguchi method

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