Improved rule discovery performance on uncertainty

Mehmet R. Tolun; Hayri Sever; Mahmut Uludağ

doi:10.1007/3-540-64383-4_26

Improved rule discovery performance on uncertainty

Mehmet R. Tolun, Hayri Sever, Mahmut Uludağ

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

1 Scopus citations

Abstract

In this paper we describe the improved version of a novel rule induction algorithm, namely ILA. We first outline the basic algorithm, and then present how the algorithm is enhanced using the new evaluation metric that handles uncertainty in a given data set. In addition to having a faster induction than the original one, we believe that our contribution comes into picture with a new metric that allows users to define their preferences through a penalty factor. We use this penalty factor to tackle with over-fitting bias, which is inherently found in a great many of inductive algorithms. We compare the improved algorithm ILA-2 to a variety of induction algorithms, including ID3, OCI, C4.5, CN2, and ILA. According to our preliminary experimental work, the algorithm appears to be comparable to the well-known algorithms such as CN2 and C4.5 in terms of accuracy and size.

Original language	English (US)
Title of host publication	Research and Development in Knowledge Discovery and Data Mining - 2nd Pacific-Asia Conference, PAKDD 1998, Proceedings
Editors	Xindong Wu, Ramamohanarao Kotagiri, Kevin B. Korb
Publisher	Springer Verlag
Pages	310-321
Number of pages	12
ISBN (Print)	3540643834, 9783540643838
DOIs	https://doi.org/10.1007/3-540-64383-4_26
State	Published - 1998
Externally published	Yes
Event	2nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1998 - Melbourne, Australia Duration: Apr 15 1998 → Apr 17 1998

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	1394
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1998
Country/Territory	Australia
City	Melbourne
Period	04/15/98 → 04/17/98

Bibliographical note

Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 1998.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/3-540-64383-4_26

Cite this

Tolun, M. R., Sever, H., & Uludağ, M. (1998). Improved rule discovery performance on uncertainty. In X. Wu, R. Kotagiri, & K. B. Korb (Eds.), Research and Development in Knowledge Discovery and Data Mining - 2nd Pacific-Asia Conference, PAKDD 1998, Proceedings (pp. 310-321). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1394). Springer Verlag. https://doi.org/10.1007/3-540-64383-4_26

Tolun, Mehmet R. ; Sever, Hayri ; Uludağ, Mahmut. / Improved rule discovery performance on uncertainty. Research and Development in Knowledge Discovery and Data Mining - 2nd Pacific-Asia Conference, PAKDD 1998, Proceedings. editor / Xindong Wu ; Ramamohanarao Kotagiri ; Kevin B. Korb. Springer Verlag, 1998. pp. 310-321 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper we describe the improved version of a novel rule induction algorithm, namely ILA. We first outline the basic algorithm, and then present how the algorithm is enhanced using the new evaluation metric that handles uncertainty in a given data set. In addition to having a faster induction than the original one, we believe that our contribution comes into picture with a new metric that allows users to define their preferences through a penalty factor. We use this penalty factor to tackle with over-fitting bias, which is inherently found in a great many of inductive algorithms. We compare the improved algorithm ILA-2 to a variety of induction algorithms, including ID3, OCI, C4.5, CN2, and ILA. According to our preliminary experimental work, the algorithm appears to be comparable to the well-known algorithms such as CN2 and C4.5 in terms of accuracy and size.",

author = "Tolun, {Mehmet R.} and Hayri Sever and Mahmut Uludağ",

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Tolun, MR, Sever, H & Uludağ, M 1998, Improved rule discovery performance on uncertainty. in X Wu, R Kotagiri & KB Korb (eds), Research and Development in Knowledge Discovery and Data Mining - 2nd Pacific-Asia Conference, PAKDD 1998, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1394, Springer Verlag, pp. 310-321, 2nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 1998, Melbourne, Australia, 04/15/98. https://doi.org/10.1007/3-540-64383-4_26

Improved rule discovery performance on uncertainty. / Tolun, Mehmet R.; Sever, Hayri; Uludağ, Mahmut.
Research and Development in Knowledge Discovery and Data Mining - 2nd Pacific-Asia Conference, PAKDD 1998, Proceedings. ed. / Xindong Wu; Ramamohanarao Kotagiri; Kevin B. Korb. Springer Verlag, 1998. p. 310-321 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1394).

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

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AB - In this paper we describe the improved version of a novel rule induction algorithm, namely ILA. We first outline the basic algorithm, and then present how the algorithm is enhanced using the new evaluation metric that handles uncertainty in a given data set. In addition to having a faster induction than the original one, we believe that our contribution comes into picture with a new metric that allows users to define their preferences through a penalty factor. We use this penalty factor to tackle with over-fitting bias, which is inherently found in a great many of inductive algorithms. We compare the improved algorithm ILA-2 to a variety of induction algorithms, including ID3, OCI, C4.5, CN2, and ILA. According to our preliminary experimental work, the algorithm appears to be comparable to the well-known algorithms such as CN2 and C4.5 in terms of accuracy and size.

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Tolun MR, Sever H, Uludağ M. Improved rule discovery performance on uncertainty. In Wu X, Kotagiri R, Korb KB, editors, Research and Development in Knowledge Discovery and Data Mining - 2nd Pacific-Asia Conference, PAKDD 1998, Proceedings. Springer Verlag. 1998. p. 310-321. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/3-540-64383-4_26

Improved rule discovery performance on uncertainty

Abstract

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