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Classification with Weighted C4.5 Decision Tree Approach
http://hdl.handle.net/20.500.12678/0000006191
http://hdl.handle.net/20.500.12678/00000061919a669a2c-ff6b-46d8-9cc1-d9809bf6dba3
0bdf5377-bdff-4355-8fd6-cc974c3df860
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Publication type | ||||||
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Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | Classification with Weighted C4.5 Decision Tree Approach | |||||
Language | en | |||||
Publication date | 2019-09-02 | |||||
Authors | ||||||
Khin Thuzar Win | ||||||
Aung Nway Oo | ||||||
Description | ||||||
Data mining techniques is increasing becoming on medical data for discovering useful trends and patterns that are used in diagnosis and decision making. Classification is a data mining technique which addresses the problem of constructing a predictive model for a class attribute given the values of other attributes and some examples of records with known class. This paper we implemented the weighted C4.5 decision tree algorithms for Breast Cancer classification. Naïve Bayesian theorem was used to calculate the weight value to set the appropriate weights of training instances before trying to construct a decision tree model. The research work focuses the predictive comparative analysis of weighted C4.5 decision tree algorithm with traditional C4.5 decision tree algorithm by using Breast Cancer Datasets. | ||||||
Keywords | ||||||
Data mining (DM), Classification, Decision Tree (DT), C4.5 | ||||||
Conference papers | ||||||
PSC | ||||||
September, 2019 | ||||||
The Local Conference on Parallel and Soft Computing | ||||||
Yangon, Myanmar |