A review of homogenous ensemble methods on the classification of breast cancer data
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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SIGMA BOT
2024
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| Online Access: | https://umpir.ump.edu.my/id/eprint/40124/ |
| _version_ | 1848827304618229760 |
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| author | Nur Farahaina, Idris Mohd Arfian, Ismail |
| author_facet | Nur Farahaina, Idris Mohd Arfian, Ismail |
| author_sort | Nur Farahaina, Idris |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:37:12Z |
| format | Article |
| id | ump-40124 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:58:35Z |
| publishDate | 2024 |
| publisher | SIGMA BOT |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-401242025-08-18T04:09:29Z https://umpir.ump.edu.my/id/eprint/40124/ A review of homogenous ensemble methods on the classification of breast cancer data Nur Farahaina, Idris Mohd Arfian, Ismail QA75 Electronic computers. Computer science RC0254 Neoplasms. Tumors. Oncology (including Cancer) 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. SIGMA BOT 2024 Article PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/40124/1/A%20review%20of%20homogenous%20ensemble%20methods.pdf Nur Farahaina, Idris and Mohd Arfian, Ismail (2024) A review of homogenous ensemble methods on the classification of breast cancer data. Przegląd Elektrotechniczny, 2024 (1). pp. 101 -104. ISSN 0033-2097. (Published) https://doi.org/10.15199/48.2024.01.21 https://doi.org/10.15199/48.2024.01.21 https://doi.org/10.15199/48.2024.01.21 |
| spellingShingle | QA75 Electronic computers. Computer science RC0254 Neoplasms. Tumors. Oncology (including Cancer) Nur Farahaina, Idris Mohd Arfian, Ismail A review of homogenous ensemble methods on the classification of breast cancer data |
| title | A review of homogenous ensemble methods on the classification of breast cancer data |
| title_full | A review of homogenous ensemble methods on the classification of breast cancer data |
| title_fullStr | A review of homogenous ensemble methods on the classification of breast cancer data |
| title_full_unstemmed | A review of homogenous ensemble methods on the classification of breast cancer data |
| title_short | A review of homogenous ensemble methods on the classification of breast cancer data |
| title_sort | review of homogenous ensemble methods on the classification of breast cancer data |
| topic | QA75 Electronic computers. Computer science RC0254 Neoplasms. Tumors. Oncology (including Cancer) |
| url | https://umpir.ump.edu.my/id/eprint/40124/ https://umpir.ump.edu.my/id/eprint/40124/ https://umpir.ump.edu.my/id/eprint/40124/ |