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FEATURE SELECTION FOR KNOWLEDGE DISCOVERY AND DATA MINING
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FEATURE SELECTION FOR KNOWLEDGE DISCOVERY AND DATA MINING
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FEATURE SELECTION
FOR
KNOWLEDGE DISCOVERY AND
DATA MINING

THE
KLUWER INTERNATIONAL SERIES
IN ENGINEERING AND COMPUTER SCIENCE

Library
of
Congress Cataloging.in.Publication Data
Liu, Huan.
Feature selection for knowledge discovery and data mining / by
Huan Liu and Hiroshi Motoda.
p. cm. -- (Kluwer international series in engineering and
computer science ; 454)
Inc1udes bibliographical references and index.
ISBN 978-1-4613-7604-0
ISBN
978-1-4615-5689-3 (eBook)
DOI
10.1007/978-1-4615-5689-3
1.
Database management. 2. Data mining.
1.
Motoda, Hiroshi.
II.
Title. III. Series : Kluwer international series in engineering
and computer science ; SECS 454.
QA76.9.D3L595 1998
006.3--dc21
Copyright el 1998 by Springer Science+Business Media
New
York
98-25204
CIP
Originally published by Kluwer Academic Publishers, New York in 1998. Second
Printing 2000.
Softcover reprint
ofthe
hardcover
Ist
edition 1998
AH
rights reserved. No part of this publication may
be
reproduced, stored in a
retrieval system or transmitted in any form or
by
any means, mechanical, photo-
copying, recording, or otherwise, without the prior written permission of the publisher,
Springer Science+Business Media, LLC.
Printed an acid-free paper.

FEATURE
SELECTION
FOR
KNOWLEDGE DISCOVERY AND
DATAMINING
by
Huan Liu
National University
of
Singapore
SINGAPORE
and
Hiroshi Motoda
Osaka University
Osaka,
JAPAN
~.
"
SPRINGER
SCIENCE+BUSINESS
MEDIA,
LLC

Contents
List
of
Figu
res
xi
List
of
Tables
xv
Preface
xix
Acknowledgments
xxiii
1.
DATA
PROCESSING
AND
KDD
1
1.1
Inductive
Learning
from
Observation 2
1.1.1 Features 2
1.1.2
Feature-based
classification
5
1.2
Knowledge
Discovery
and
Data
Mining
6
1.2.1
Data
mining
- a
new
challenge
7
1.3
Feature
Selection
and
Its
Roles
in
KDD
9
1.4 Summary
12
References 13
2.
PERSPECTIVES
OF
FEATURE
SELECTION
17
2.1 Feature
Selection
for
Classification
18
2.2
A Search
Problem
20
2.2.1 Search directions
20
2.2.2
Search
strategies
22
2.3 Selection Criteria
24
2.3.1 The
ideal
classifier
25
2.3.2
Information
measures
26
2.3.3 Distance measures
27
2.3.4 Dependence measures
27
2.3.5
Consistency
measures
28
2.3.6
Accuracy
measures
28
2.3.7
Discussion
28
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