Abstract
Classification of datasets with characteristics such as high dimensionality and class imbalance is a major challenge in the field of data mining. Hence to restructure data, a synthetic minority over sampling technique (SMOTE) was chosen to balance the dataset. To solve the problem of high dimensionality feature extraction, principal component analysis (PCA) was adopted. Usually a single classifier is biased. To reduce the variance and bias of a single classifier an ensemble approach, i.e. the learning of multiple classifiers was tested. In this study, the experimental results of a hybrid approach, i.e. PCA with SMOTE and an ensemble approach of the best classifiers obtained from PCA with SMOTE was analyzed by choosing five diverse classifiers of breast cancer datasets.
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Naga RamaDevi, G., Usha Rani, K., Lavanya, D. (2019). Ensemble-Based Hybrid Approach for Breast Cancer Data. In: Kumar, A., Mozar, S. (eds) ICCCE 2018. ICCCE 2018. Lecture Notes in Electrical Engineering, vol 500. Springer, Singapore. https://doi.org/10.1007/978-981-13-0212-1_72
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DOI: https://doi.org/10.1007/978-981-13-0212-1_72
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