A review of instance selection methods

被引：269

作者：

Arturo Olvera-Lopez, J. ^{[1
]}

Ariel Carrasco-Ochoa, J. ^{[2
]}

Francisco Martinez-Trinidad, J. ^{[2
]}

Kittler, Josef ^{[3
]}

机构：

[1] Benemerita Univ Autonoma Puebla, Fac Ciencias Comp, Puebla 72570, Mexico

[2] Natl Inst Astrophys Opt & Elect, Dept Comp Sci, Puebla 72000, Mexico

[3] Univ Surrey, Ctr Vis Speech & Signal Proc, Guildford GU2 7XH, Surrey, England

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2010年 / 34卷 / 02期

关键词：

Instance selection; Supervised learning; Data reduction; Pre-processing; PROTOTYPE SELECTION; NEAREST; REDUCTION; ALGORITHM; SEARCH; CLASSIFICATION; SUBSET; RULE;

D O I：

10.1007/s10462-010-9165-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

140502 [人工智能];

摘要：

In supervised learning, a training set providing previously known information is used to classify new instances. Commonly, several instances are stored in the training set but some of them are not useful for classifying therefore it is possible to get acceptable classification rates ignoring non useful cases; this process is known as instance selection. Through instance selection the training set is reduced which allows reducing runtimes in the classification and/or training stages of classifiers. This work is focused on presenting a survey of the main instance selection methods reported in the literature.

引用

页码：133 / 143

页数：11

共 48 条

[1]

AHA DW, 1991, MACH LEARN, V6, P37, DOI 10.1007/BF00153759

[2]

Nearest prototype classifier designs: An experimental study [J].

Bezdek, JC ;

Kuncheva, LI .

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2001, 16 (12) :1445-1473

[3]

Selection of relevant features and examples in machine learning [J].

Blum, AL ;

Langley, P .

ARTIFICIAL INTELLIGENCE, 1997, 97 (1-2) :245-271

[4]

Advances in instance selection for instance-based learning algorithms [J].

Brighton, H ;

Mellish, C .

DATA MINING AND KNOWLEDGE DISCOVERY, 2002, 6 (02) :153-172

[5]

Caises Y, 2009, LECT NOTES COMPUT SC, V5788, P17, DOI 10.1007/978-3-642-04394-9_3

[6]

Stratification for scaling up evolutionary prototype selection [J].

Cano, JR ;

Herrera, F ;

Lozano, M .

PATTERN RECOGNITION LETTERS, 2005, 26 (07) :953-963

[7]

Using evolutionary algorithms as instance selection for data reduction in KDD: An experimental study [J].

Cano, JR ;

Herrera, F ;

Lozano, M .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2003, 7 (06) :561-575

[8]

Another move toward the minimum consistent subset:: A tabu search approach to the condensed nearest neighbor rule [J].

Cerverón, V ;

Ferri, FJ .

IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2001, 31 (03) :408-413

[9]

Chou CH, 2006, INT C PATT RECOG, P556

[10]

NEAREST NEIGHBOR PATTERN CLASSIFICATION [J].

COVER, TM ;

HART, PE .

IEEE TRANSACTIONS ON INFORMATION THEORY, 1967, 13 (01) :21-+

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