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Issues in Optimization of Decision Tree Learning: A Survey

Dipak V. Patil, R. S. Bichkar Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
10.5120/ijais12-450512
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  1. Dipak V Patil and R S Bichkar. Article: Issues in Optimization of Decision Tree Learning: A Survey. International Journal of Applied Information Systems 3(5):13-29, July 2012. BibTeX

    @article{key:article,
    	author = "Dipak V. Patil and R. S. Bichkar",
    	title = "Article: Issues in Optimization of Decision Tree Learning: A Survey",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 5,
    	pages = "13-29",
    	month = "July",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Decision tree induction is a simple but powerful learning and classification model. Decision tree learning offers tools for discovery of relationships, patterns and knowledge from data in databases. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Decision tree learning from a very large set of records in a database is quite complex task and is usually a very slow process, which is often beyond the capabilities of existing computers. There are various issues and problems related to decision trees. To handle these issues various approaches have been proposed in the past by different researchers. This paper is an attempt to summarize the proposed approaches, tools etc. for decision tree learning with emphasis on optimization of constructed trees and handling large datasets.

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Keywords

Decision Tree, Optimization