International Journal of Innovative Research in Engineering and Management
Year: 2016, Volume: 3, Issue: 2
First page : ( 119) Last page : ( 121)
Online ISSN : 2350-0557.
Prof. Anil Hingmire , Nikita M. Chaudahri , Utkarsha P. Patil , Lalita S. Mahajan Computer Engineering Department,VCET, Vasai,Maharashtra India lmahajan14@gmai
Decision tree learning algorithm has been successfully used in expert systems in capturing knowledge. The main task performed in these systems issuing inductive methods to the given values of attributes of an unknown object to determine appropriate classification according to decision tree rules .It is one of the most effective forms to represent and evaluate the performance of algorithms, due to its various eye catching features: simplicity, comprehensibility, no parameters, and being able to handle mixed-type data. There are many decision two algorithm available named ID3, C4.5, CART, CHAID, QUEST, GUIDE, CRUISE, and CTREE. We have explained three most commonly used decision tree algorithm in this paper to understand their use and scalability on different types of attributes and feature. ID3(Iterative Dichotomizer 3) developed by J.R Quinlan in 1986, CART stands for Classification and Regression Trees developed by Breiman et al.in 1984).
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Computer Engineering Department.,VCET, Vasai,Maharashtra India
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