Abstract
Data Mining is the process of examining huge pre-existing databases in order to produce new information. Decision Tree is a very popular and practical approach in Data Mining. Decision Trees plays a crucial role in medical field. Ensemble methods create multiple models and produce more accurate solutions than a single model. Feature Selection improves the standard of the data by removing irrelevant attributes, due to that accuracy will increase. In this study experiments are conducted on a Hybrid Method i.e., C4.5 Decision Tree with MultiBoostAB Ensemble technique with different Feature Selection Techniques on Tumor datasets for finding out accuracy, execution time to build a decision tree and size of the tree.
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Sujatha, G., Usha Rani, K. (2020). A Comprehensive Hybrid Ensemble Method with Feature Selection Techniques. In: Jyothi, S., Mamatha, D., Satapathy, S., Raju, K., Favorskaya, M. (eds) Advances in Computational and Bio-Engineering. CBE 2019. Learning and Analytics in Intelligent Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-030-46939-9_8
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DOI: https://doi.org/10.1007/978-3-030-46939-9_8
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