2026-05-01T09:22:37Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/6191 2021-12-13T05:07:48Z 1582963342780:1596102355557 user-uit

Classification with Weighted C4.5 Decision Tree Approach Khin Thuzar Win Aung Nway Oo 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. 2019-09-02 http://hdl.handle.net/20.500.12678/0000006191 https://meral.edu.mm/records/6191