A less-greedy two-term Tsallis Entropy Information Metric approach for decision tree classification

Wang, Y; Xia, ST; Wu, J

A less-greedy two-term Tsallis Entropy Information Metric approach for decision tree classification

Wang, Y Xia, ST Wu, J

Permalink

Publication Type:: Journal Article
Citation:: Knowledge-Based Systems, 2017, 120 pp. 34 - 42
Issue Date:: 2017-03-15

Closed Access

	Filename	Description	Size
	1-s2.0-S095070511630524X-main.pdf	Published Version	755.67 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Xia, ST	en_US
dc.contributor.author	Wu, J	en_US
dc.date.issued	2017-03-15	en_US
dc.identifier.citation	Knowledge-Based Systems, 2017, 120 pp. 34 - 42	en_US
dc.identifier.issn	0950-7051	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127752
dc.description.abstract	© 2016 The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. Most of them, however, are greedy algorithms that have the drawback of obtaining only local optimums. Besides, conventional split criteria they used, e.g. Shannon entropy, Gain Ratio and Gini index, are based on one-term that lack adaptability to different datasets. To address the above issues, we propose a less-greedy two-term Tsallis Entropy Information Metric (TEIM) algorithm with a new split criterion and a new construction method of decision trees. Firstly, the new split criterion is based on two-term Tsallis conditional entropy, which is better than conventional one-term split criteria. Secondly, the new tree construction is based on a two-stage approach that reduces the greediness and avoids local optimum to a certain extent. The TEIM algorithm takes advantages of the generalization ability of two-term Tsallis entropy and the low greediness property of two-stage approach. Experimental results on UCI datasets indicate that, compared with the state-of-the-art decision trees algorithms, the TEIM algorithm yields statistically significantly better decision trees and is more robust to noise.	en_US
dc.relation.ispartof	Knowledge-Based Systems	en_US
dc.relation.isbasedon	10.1016/j.knosys.2016.12.021	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A less-greedy two-term Tsallis Entropy Information Metric approach for decision tree classification	en_US
dc.type	Journal Article
utslib.citation.volume	120	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	1501 Accounting, Auditing and Accountability	en_US
utslib.for	1702 Cognitive Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	120	en_US

Abstract:

© 2016 The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. Most of them, however, are greedy algorithms that have the drawback of obtaining only local optimums. Besides, conventional split criteria they used, e.g. Shannon entropy, Gain Ratio and Gini index, are based on one-term that lack adaptability to different datasets. To address the above issues, we propose a less-greedy two-term Tsallis Entropy Information Metric (TEIM) algorithm with a new split criterion and a new construction method of decision trees. Firstly, the new split criterion is based on two-term Tsallis conditional entropy, which is better than conventional one-term split criteria. Secondly, the new tree construction is based on a two-stage approach that reduces the greediness and avoids local optimum to a certain extent. The TEIM algorithm takes advantages of the generalization ability of two-term Tsallis entropy and the low greediness property of two-stage approach. Experimental results on UCI datasets indicate that, compared with the state-of-the-art decision trees algorithms, the TEIM algorithm yields statistically significantly better decision trees and is more robust to noise.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/127752