Breast Cancer Diagnosis Using Bagging Decision Trees with Improved Feature Selection - Deepak Dudeja; Ajit Noonia; S. Lavanya; Vandana Sharma; Varun Kumar; Sumaiya Rehan; R. Ramkumar (Engineering Proceedings)
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.