Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection
Machine learning is a science of computer algorithms that enable systems to automatically learn actions and adjust them without explicit programming and improve from experience using pattern recognition. This work offers a practical introduction to the core concepts and principles of bagging decisio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/59/1/17 |
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| author | Deepak Dudeja Ajit Noonia S. Lavanya Vandana Sharma Varun Kumar Sumaiya Rehan R. Ramkumar |
| author_facet | Deepak Dudeja Ajit Noonia S. Lavanya Vandana Sharma Varun Kumar Sumaiya Rehan R. Ramkumar |
| author_sort | Deepak Dudeja |
| collection | DOAJ |
| description | Machine learning is a science of computer algorithms that enable systems to automatically learn actions and adjust them without explicit programming and improve from experience using pattern recognition. This work offers a practical introduction to the core concepts and principles of bagging decision trees used for breast cancer diagnosis. In this article, three main algorithms, viz. linear regression (LR), decision tree (DT), and random forest, were used. The random forest method used bagging techniques for selecting data points, and feature optimization was also carried out. Through our experiments, it has been found that the results obtained with the bagging trees algorithm outperform the result obtained with the best decision tree parameters. A feature optimization scheme was also introduced in the selection of data points during the training phase, which effectively increased accuracy. |
| format | Article |
| id | doaj-art-8e87a2a7e0fd470abbcc0421b6a09fe8 |
| institution | OA Journals |
| issn | 2673-4591 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-8e87a2a7e0fd470abbcc0421b6a09fe82025-08-20T02:11:01ZengMDPI AGEngineering Proceedings2673-45912023-12-015911710.3390/engproc2023059017Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature SelectionDeepak Dudeja0Ajit Noonia1S. Lavanya2Vandana Sharma3Varun Kumar4Sumaiya Rehan5R. Ramkumar6Department of Computer Science and Engineering, Maharishi Markandeshwar Deemed to be University, Ambala 133203, IndiaDepartment of Computer Science and Engineering, Manipal University Jaipur, Jaipur 302034, IndiaDepartment of Computer Science and Engineering, R.V.S. College of Engineering, RVS Nagar, Dindigul 624005, IndiaDepartment of Computer Science, ABES Engineering College, Ghaziabad 201009, IndiaDepartment of Mathematics, School of Arts and Sciences, University of the People, Pasadena, CA 00012, USADepartment of Computer Science and Engineering, BBD University, Lucknow 226010, IndiaDepartment of Electrical and Electronics Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram 621112, IndiaMachine learning is a science of computer algorithms that enable systems to automatically learn actions and adjust them without explicit programming and improve from experience using pattern recognition. This work offers a practical introduction to the core concepts and principles of bagging decision trees used for breast cancer diagnosis. In this article, three main algorithms, viz. linear regression (LR), decision tree (DT), and random forest, were used. The random forest method used bagging techniques for selecting data points, and feature optimization was also carried out. Through our experiments, it has been found that the results obtained with the bagging trees algorithm outperform the result obtained with the best decision tree parameters. A feature optimization scheme was also introduced in the selection of data points during the training phase, which effectively increased accuracy.https://www.mdpi.com/2673-4591/59/1/17machine learningdeep learning (DL)convolution neural network (CNN)xceptionNetleNetdenseNet |
| spellingShingle | Deepak Dudeja Ajit Noonia S. Lavanya Vandana Sharma Varun Kumar Sumaiya Rehan R. Ramkumar Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection Engineering Proceedings machine learning deep learning (DL) convolution neural network (CNN) xceptionNet leNet denseNet |
| title | Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection |
| title_full | Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection |
| title_fullStr | Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection |
| title_full_unstemmed | Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection |
| title_short | Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection |
| title_sort | breast cancer diagnosis using bagging decision trees with improved feature selection |
| topic | machine learning deep learning (DL) convolution neural network (CNN) xceptionNet leNet denseNet |
| url | https://www.mdpi.com/2673-4591/59/1/17 |
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