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  1. University of Information Technology
  2. Faculty of Computer Science

Classification with Weighted C4.5 Decision Tree Approach

http://hdl.handle.net/20.500.12678/0000006191
http://hdl.handle.net/20.500.12678/0000006191
9a669a2c-ff6b-46d8-9cc1-d9809bf6dba3
0bdf5377-bdff-4355-8fd6-cc974c3df860
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Classification Classification with Weighted C4.5 Decision Tree Approach.pdf (371 Kb)
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Publication type
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
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