Lazy classification algorithms based on deterministic and inhibitory association rules

Pawel Delimata; Mikhail Ju Moshkov; Andrzej Skowron; Zbigniew Suraj

doi:10.1007/978-3-540-85638-2_7

Lazy classification algorithms based on deterministic and inhibitory association rules

Pawel Delimata, Mikhail Ju Moshkov, Andrzej Skowron, Zbigniew Suraj

Research output: Contribution to journal › Article › peer-review

Abstract

In this chapter, we consider the same classification problem as in Chap. 5: for a given decision table T and a new object v it is required to generate a value of the decision attribute on v using values of conditional attributes on v. To this end, we divide the decision table T into a number of information systems Si, i ∈ Dec (T), where Dec (T) is the set of values of the decision attribute in T. For i ∈ Dec (T), the information system Si contains only objects (rows) of T with the value of the decision attribute equal to i. © 2009 Springer-Verlag Berlin Heidelberg.

Original language	English (US)
Pages (from-to)	87-97
Number of pages	11
Journal	Studies in Computational Intelligence
Volume	163
DOIs	https://doi.org/10.1007/978-3-540-85638-2_7
State	Published - Jan 1 2009
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2023-09-21

ASJC Scopus subject areas

Artificial Intelligence

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10.1007/978-3-540-85638-2_7

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abstract = "In this chapter, we consider the same classification problem as in Chap. 5: for a given decision table T and a new object v it is required to generate a value of the decision attribute on v using values of conditional attributes on v. To this end, we divide the decision table T into a number of information systems Si, i ∈ Dec (T), where Dec (T) is the set of values of the decision attribute in T. For i ∈ Dec (T), the information system Si contains only objects (rows) of T with the value of the decision attribute equal to i. {\textcopyright} 2009 Springer-Verlag Berlin Heidelberg.",

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AU - Delimata, Pawel

AU - Moshkov, Mikhail Ju

AU - Skowron, Andrzej

AU - Suraj, Zbigniew

N1 - Generated from Scopus record by KAUST IRTS on 2023-09-21

PY - 2009/1/1

Y1 - 2009/1/1

N2 - In this chapter, we consider the same classification problem as in Chap. 5: for a given decision table T and a new object v it is required to generate a value of the decision attribute on v using values of conditional attributes on v. To this end, we divide the decision table T into a number of information systems Si, i ∈ Dec (T), where Dec (T) is the set of values of the decision attribute in T. For i ∈ Dec (T), the information system Si contains only objects (rows) of T with the value of the decision attribute equal to i. © 2009 Springer-Verlag Berlin Heidelberg.

AB - In this chapter, we consider the same classification problem as in Chap. 5: for a given decision table T and a new object v it is required to generate a value of the decision attribute on v using values of conditional attributes on v. To this end, we divide the decision table T into a number of information systems Si, i ∈ Dec (T), where Dec (T) is the set of values of the decision attribute in T. For i ∈ Dec (T), the information system Si contains only objects (rows) of T with the value of the decision attribute equal to i. © 2009 Springer-Verlag Berlin Heidelberg.

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JO - Studies in Computational Intelligence

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ER -

Lazy classification algorithms based on deterministic and inhibitory association rules

Abstract

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