A New Searching Method of Splitting Threshold Values for Continuous Attribute Decision Tree Problems

Kuang Yow Lian; Ru Feng Liu

doi:10.1109/SMC.2015.207

A New Searching Method of Splitting Threshold Values for Continuous Attribute Decision Tree Problems

Kuang Yow Lian
, Ru Feng Liu

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

In the paper, we extend the well-known golden-section search (GSS) method to make an unprecedented attempt to do discrete sequence searches. The GSS method is originally used to find the extremum of a strictly unimodal continuous function. We apply it on searching the best threshold for discretizing continuous attribute data in decision tree problems. Compared to typical methods, the shortcomings relating to massive calculation requirements for searching threshold values are eliminated. Whether it is used along with information gain or Gini index as the measure indicator for data purity of decision tree, the algorithm produces good results. To verify the proposed method, data set provided by UCI database is used on Mat lab platform to carry out the simulation. Results indicate that under the same performance index, the discrete GSS method significantly lowers iteration numbers of searching threshold values and, hence, verify the feasibility of this algorithm.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1157-1160
Number of pages	4
ISBN (Electronic)	9781479986965
DOIs	https://doi.org/10.1109/SMC.2015.207
State	Published - 12 Jan 2016
Event	IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong Duration: 9 Oct 2015 → 12 Oct 2015

Publication series

Name	Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

Conference

Conference	IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Country/Territory	Hong Kong
City	Kowloon Tong
Period	9/10/15 → 12/10/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

continuous attributes
decision tree
golden section search
machine learning

Access to Document

10.1109/SMC.2015.207

Cite this

Lian, K. Y., & Liu, R. F. (2016). A New Searching Method of Splitting Threshold Values for Continuous Attribute Decision Tree Problems. In Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 (pp. 1157-1160). Article 7379339 (Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2015.207

Lian, Kuang Yow ; Liu, Ru Feng. / A New Searching Method of Splitting Threshold Values for Continuous Attribute Decision Tree Problems. Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1157-1160 (Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015).

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abstract = "In the paper, we extend the well-known golden-section search (GSS) method to make an unprecedented attempt to do discrete sequence searches. The GSS method is originally used to find the extremum of a strictly unimodal continuous function. We apply it on searching the best threshold for discretizing continuous attribute data in decision tree problems. Compared to typical methods, the shortcomings relating to massive calculation requirements for searching threshold values are eliminated. Whether it is used along with information gain or Gini index as the measure indicator for data purity of decision tree, the algorithm produces good results. To verify the proposed method, data set provided by UCI database is used on Mat lab platform to carry out the simulation. Results indicate that under the same performance index, the discrete GSS method significantly lowers iteration numbers of searching threshold values and, hence, verify the feasibility of this algorithm.",

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Lian, KY & Liu, RF 2016, A New Searching Method of Splitting Threshold Values for Continuous Attribute Decision Tree Problems. in Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015., 7379339, Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1157-1160, IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, Kowloon Tong, Hong Kong, 9/10/15. https://doi.org/10.1109/SMC.2015.207

A New Searching Method of Splitting Threshold Values for Continuous Attribute Decision Tree Problems. / Lian, Kuang Yow; Liu, Ru Feng.
Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1157-1160 7379339 (Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Lian KY, Liu RF. A New Searching Method of Splitting Threshold Values for Continuous Attribute Decision Tree Problems. In Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1157-1160. 7379339. (Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015). doi: 10.1109/SMC.2015.207

A New Searching Method of Splitting Threshold Values for Continuous Attribute Decision Tree Problems

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