An efficient attribute reduction algorithm

Yuguo He

doi:10.1007/11875581_103

An efficient attribute reduction algorithm

Yuguo He^*

^*Corresponding author for this work

School of Computer Science and Technology

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

1 Citation (Scopus)

Abstract

Attribute reduction is an important issue of data mining. It is generally regarded as a preprocessing phase that alleviates the curse of dimensionality, though it also leads to classificatory analysis of decision tables. In this paper, we propose an efficient algorithm TWI-SQUEEZE that can find a minimal (or irreducible) attribute subset, which preserves classificatory consistency after two scans of a decision table. Its worst-case computational complexity is analyzed. The outputs of the algorithm are two different kinds of classifiers. One is an IF-THEN rule system. The other is a decision tree.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings
Publisher	Springer Verlag
Pages	859-868
Number of pages	10
ISBN (Print)	3540454853, 9783540454854
DOIs	https://doi.org/10.1007/11875581_103
Publication status	Published - 2006
Event	7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 - Burgos, Spain Duration: 20 Sept 2006 → 23 Sept 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4224 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006
Country/Territory	Spain
City	Burgos
Period	20/09/06 → 23/09/06

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10.1007/11875581_103

Cite this

He, Y. (2006). An efficient attribute reduction algorithm. In Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings (pp. 859-868). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4224 LNCS). Springer Verlag. https://doi.org/10.1007/11875581_103

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title = "An efficient attribute reduction algorithm",

abstract = "Attribute reduction is an important issue of data mining. It is generally regarded as a preprocessing phase that alleviates the curse of dimensionality, though it also leads to classificatory analysis of decision tables. In this paper, we propose an efficient algorithm TWI-SQUEEZE that can find a minimal (or irreducible) attribute subset, which preserves classificatory consistency after two scans of a decision table. Its worst-case computational complexity is analyzed. The outputs of the algorithm are two different kinds of classifiers. One is an IF-THEN rule system. The other is a decision tree.",

author = "Yuguo He",

year = "2006",

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note = "7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006 ; Conference date: 20-09-2006 Through 23-09-2006",

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He, Y 2006, An efficient attribute reduction algorithm. in Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4224 LNCS, Springer Verlag, pp. 859-868, 7th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2006, Burgos, Spain, 20/09/06. https://doi.org/10.1007/11875581_103

An efficient attribute reduction algorithm. / He, Yuguo.
Intelligent Data Engineering and Automated Learning, IDEAL 2006 - 7th International Conference, Proceedings. Springer Verlag, 2006. p. 859-868 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4224 LNCS).

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

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AB - Attribute reduction is an important issue of data mining. It is generally regarded as a preprocessing phase that alleviates the curse of dimensionality, though it also leads to classificatory analysis of decision tables. In this paper, we propose an efficient algorithm TWI-SQUEEZE that can find a minimal (or irreducible) attribute subset, which preserves classificatory consistency after two scans of a decision table. Its worst-case computational complexity is analyzed. The outputs of the algorithm are two different kinds of classifiers. One is an IF-THEN rule system. The other is a decision tree.

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An efficient attribute reduction algorithm

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