Evaluation of decision table decomposition using dynamic programming classifiers

Michal Mankowski; Tadeusz Luba; Cezary Jankowski

Evaluation of decision table decomposition using dynamic programming classifiers

Michal Mankowski, Tadeusz Luba, Cezary Jankowski

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

Abstract

Decision table decomposition is a method that decomposes given decision table into an equivalent set of decision tables. Decomposition can enhance the quality of knowledge discovered from databases by simplifying the data mining task. The paper contains a description of decision table decomposition method and their evaluation for data classification. Additionally, a novel method of obtaining attributes sets for decomposition was introduced. Experimental results demonstrated that decomposition can reduce memory requirements preserving the accuracy of classification.

Original language	English (US)
Title of host publication	24th International Workshop on Concurrency, Specification and Programming, CS and P 2015
Publisher	CEUR-WS
Pages	34-43
Number of pages	10
ISBN (Print)	9788379961818
State	Published - Jan 1 2015
Externally published	Yes

Cite this

@inproceedings{9cca431ee5364b9fa3694bab86cdd73d,

title = "Evaluation of decision table decomposition using dynamic programming classifiers",

abstract = "Decision table decomposition is a method that decomposes given decision table into an equivalent set of decision tables. Decomposition can enhance the quality of knowledge discovered from databases by simplifying the data mining task. The paper contains a description of decision table decomposition method and their evaluation for data classification. Additionally, a novel method of obtaining attributes sets for decomposition was introduced. Experimental results demonstrated that decomposition can reduce memory requirements preserving the accuracy of classification.",

author = "Michal Mankowski and Tadeusz Luba and Cezary Jankowski",

note = "KAUST Repository Item: Exported on 2022-06-30 Acknowledgements: The authors would like to thank professor Mikhail Moshkov and his team for their support while writing this paper. This research has been supported by King Abdullah University of Science and Technology. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2015",

month = jan,

day = "1",

language = "English (US)",

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pages = "34--43",

booktitle = "24th International Workshop on Concurrency, Specification and Programming, CS and P 2015",

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T1 - Evaluation of decision table decomposition using dynamic programming classifiers

AU - Mankowski, Michal

AU - Luba, Tadeusz

AU - Jankowski, Cezary

N1 - KAUST Repository Item: Exported on 2022-06-30 Acknowledgements: The authors would like to thank professor Mikhail Moshkov and his team for their support while writing this paper. This research has been supported by King Abdullah University of Science and Technology. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2015/1/1

Y1 - 2015/1/1

N2 - Decision table decomposition is a method that decomposes given decision table into an equivalent set of decision tables. Decomposition can enhance the quality of knowledge discovered from databases by simplifying the data mining task. The paper contains a description of decision table decomposition method and their evaluation for data classification. Additionally, a novel method of obtaining attributes sets for decomposition was introduced. Experimental results demonstrated that decomposition can reduce memory requirements preserving the accuracy of classification.

AB - Decision table decomposition is a method that decomposes given decision table into an equivalent set of decision tables. Decomposition can enhance the quality of knowledge discovered from databases by simplifying the data mining task. The paper contains a description of decision table decomposition method and their evaluation for data classification. Additionally, a novel method of obtaining attributes sets for decomposition was introduced. Experimental results demonstrated that decomposition can reduce memory requirements preserving the accuracy of classification.

UR - http://hdl.handle.net/10754/679440

UR - http://www.scopus.com/inward/record.url?scp=84955496178&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9788379961818

SP - 34

EP - 43

BT - 24th International Workshop on Concurrency, Specification and Programming, CS and P 2015

PB - CEUR-WS

ER -

Evaluation of decision table decomposition using dynamic programming classifiers

Abstract

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