A review of homogenous ensemble methods on the classification of breast cancer data

• Main Authors: Nur Farahaina, Idris, Mohd Arfian, Ismail
• Format: Article
• Language: English
• Published: SIGMA BOT 2024
• Subjects: QA75 Electronic computers. Computer science; RC0254 Neoplasms. Tumors. Oncology (including Cancer)
• Online Access: http://umpir.ump.edu.my/id/eprint/40124/1/A%20review%20of%20homogenous%20ensemble%20methods.pdf; http://umpir.ump.edu.my/id/eprint/40124/; http://pe.org.pl/abstract_pl.php?nid=14124&lang=1; http://pe.org.pl/articles/2024/1/21.pdf

In the last decades, emerging data mining technology has been introduced to assist humankind in generating relevant decisions. Data mining is a concept established by computer scientists to lead a secure and reliable classification and deduction of data. In the medical field, data mining methods can assist in performing various medical diagnoses, including breast cancer. As evolution happens, ensemble methods are being proposed to achieve better performance in classification. This technique reinforced the use of multiple classifiers in the model. The review of the homogenous ensemble method on breast cancer classification is being carried out to identify the overall performance. The results of the reviewed ensemble techniques, such as Random Forest and XGBoost, show that ensemble methods can outperform the performance of the single classifier method. The reviewed ensemble methods have pros and cons and are useful for solving breast cancer classification problems. The methods are being discussed thoroughly to examine the overall performance in the classification.