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- W4285611010 abstract "AbstractBreast Cancer is a common cancer occuring among women throughout the entire world. This attributes to a majority of deaths and cases in the global statistics, which makes it a significant health problem in our society. This paper showcases the classification of breast cancer tumors and the different boosting/ensemble techniques which are used for a more accurate classification. This research presents the working and comparison between classifiers such as Bagging Meta-Estimator, ADA-Boost and XGBM. We will also be showcasing the comparison between the results of these classifiers/techniques and conclude which one is the most accurate in the classification process.KeywordsTumorsFeaturesMachine learningEnsembleMalignantBenignDatasetClassificationAccuracyClassifier" @default.
- W4285611010 created "2022-07-16" @default.
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- W4285611010 date "2022-01-01" @default.
- W4285611010 modified "2023-09-29" @default.
- W4285611010 title "Classification of Breast Tumor Using Ensemble Learning" @default.
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- W4285611010 doi "https://doi.org/10.1007/978-981-19-2069-1_34" @default.
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