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

^*Corresponding author for this work

Applied Mathematics and Computational Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter › 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 S_i, i ∈ Dec (T), where Dec (T) is the set of values of the decision attribute in T. For i ∈ Dec (T), the information system S_i contains only objects (rows) of T with the value of the decision attribute equal to i.

Original language	English (US)
Title of host publication	Inhibitory Rules in Data Analysis
Subtitle of host publication	A Rough Set Approach
Editors	Pawel Delimata, Zbigniew Suraj, Mikhail Moshkov, Andrzej Skowron
Pages	87-97
Number of pages	11
DOIs	https://doi.org/10.1007/978-3-540-85638-2_7
State	Published - 2009

Publication series

Name	Studies in Computational Intelligence
Volume	163
ISSN (Print)	1860-949X

ASJC Scopus subject areas

Artificial Intelligence

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

Cite this

Delimata, P., Moshkov, M. J., Skowron, A., & Suraj, Z. (2009). Lazy classification algorithms based on deterministic and inhibitory association rules. In P. Delimata, Z. Suraj, M. Moshkov, & A. Skowron (Eds.), Inhibitory Rules in Data Analysis: A Rough Set Approach (pp. 87-97). (Studies in Computational Intelligence; Vol. 163). https://doi.org/10.1007/978-3-540-85638-2_7

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title = "Lazy classification algorithms based on deterministic and inhibitory association rules",

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.",

author = "Pawel Delimata and Moshkov, {Mikhail Ju} and Andrzej Skowron and Zbigniew Suraj",

year = "2009",

doi = "10.1007/978-3-540-85638-2_7",

language = "English (US)",

isbn = "9783540856375",

series = "Studies in Computational Intelligence",

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editor = "Pawel Delimata and Zbigniew Suraj and Mikhail Moshkov and Andrzej Skowron",

booktitle = "Inhibitory Rules in Data Analysis",

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Lazy classification algorithms based on deterministic and inhibitory association rules. / Delimata, Pawel; Moshkov, Mikhail Ju; Skowron, Andrzej et al.
Inhibitory Rules in Data Analysis: A Rough Set Approach. ed. / Pawel Delimata; Zbigniew Suraj; Mikhail Moshkov; Andrzej Skowron. 2009. p. 87-97 (Studies in Computational Intelligence; Vol. 163).

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

TY - CHAP

T1 - Lazy classification algorithms based on deterministic and inhibitory association rules

AU - Delimata, Pawel

AU - Moshkov, Mikhail Ju

AU - Skowron, Andrzej

AU - Suraj, Zbigniew

PY - 2009

Y1 - 2009

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.

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.

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U2 - 10.1007/978-3-540-85638-2_7

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T3 - Studies in Computational Intelligence

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BT - Inhibitory Rules in Data Analysis

A2 - Delimata, Pawel

A2 - Suraj, Zbigniew

A2 - Moshkov, Mikhail

A2 - Skowron, Andrzej

ER -

Lazy classification algorithms based on deterministic and inhibitory association rules

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