Introduction

Mikhail Ju Moshkov; Marcin Piliszczuk; Beata Zielosko

doi:10.1007/978-3-540-69029-0_1

Introduction

Mikhail Ju Moshkov^*, Marcin Piliszczuk, Beata Zielosko

^*Corresponding author for this work

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

1 Scopus citations

Abstract

The subject matter of this monograph is connected with the following two problems of data mining and knowledge discovery: Representation of knowledge, contained in a decision table, in a form which is convenient for understanding. The length of knowledge description is crucial in this case. Prediction of the value of decision attribute for a new object. The accuracy of prediction is the most important aspect of this problem. These two aims (short description and high accuracy) seem to be incompatible. However, it is known that classifiers with shorter description are often more precise. This monograph is one more confirmation of this fact.

Original language	English (US)
Title of host publication	Partial Covers, Reducts and Decision Rules in Rough Sets
Subtitle of host publication	Theory and Applications
Editors	Mikhail Moshkov, Beata Zielosko, Marcin Piliszczuk
Pages	1-6
Number of pages	6
DOIs	https://doi.org/10.1007/978-3-540-69029-0_1
State	Published - 2008
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	145
ISSN (Print)	1860-949X

ASJC Scopus subject areas

Artificial Intelligence

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10.1007/978-3-540-69029-0_1

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Introduction. / Moshkov, Mikhail Ju; Piliszczuk, Marcin; Zielosko, Beata.
Partial Covers, Reducts and Decision Rules in Rough Sets: Theory and Applications. ed. / Mikhail Moshkov; Beata Zielosko; Marcin Piliszczuk. 2008. p. 1-6 (Studies in Computational Intelligence; Vol. 145).

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

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Introduction

Abstract

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