Meta analysis of classification algorithms for pattern recognition

So Young Sohn

doi:10.1109/34.809107

Meta analysis of classification algorithms for pattern recognition

So Young Sohn

Department of Industrial Engineering

Research output: Contribution to journal › Article

81 Citations (Scopus)

Abstract

Various classification algorithms became available due to a surge of interdisciplinary research interests in the areas of data mining and knowledge discovery. We develop a statistical meta-model which compares the classification performances of several algorithms in terms of data characteristics. This empirical model is expected to aid decision making processes of finding the best classification tool in the sense of providing the minimum classification error among alternatives.

Original language	English
Pages (from-to)	1137-1144
Number of pages	8
Journal	IEEE transactions on pattern analysis and machine intelligence
Volume	21
Issue number	11
DOIs	https://doi.org/10.1109/34.809107
Publication status	Published - 1999 Dec 1

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

Access to Document

10.1109/34.809107

Fingerprint Dive into the research topics of 'Meta analysis of classification algorithms for pattern recognition'. Together they form a unique fingerprint.

Cite this

Sohn, S. Y. (1999). Meta analysis of classification algorithms for pattern recognition. IEEE transactions on pattern analysis and machine intelligence, 21(11), 1137-1144. https://doi.org/10.1109/34.809107

@article{b4b4b68bb01b4021aa24cf4e1bf449c4,

title = "Meta analysis of classification algorithms for pattern recognition",

abstract = "Various classification algorithms became available due to a surge of interdisciplinary research interests in the areas of data mining and knowledge discovery. We develop a statistical meta-model which compares the classification performances of several algorithms in terms of data characteristics. This empirical model is expected to aid decision making processes of finding the best classification tool in the sense of providing the minimum classification error among alternatives.",

author = "Sohn, {So Young}",

year = "1999",

month = dec,

day = "1",

doi = "10.1109/34.809107",

language = "English",

volume = "21",

pages = "1137--1144",

journal = "IEEE Transactions on Pattern Analysis and Machine Intelligence",

issn = "0162-8828",

publisher = "IEEE Computer Society",

number = "11",

}

TY - JOUR

T1 - Meta analysis of classification algorithms for pattern recognition

AU - Sohn, So Young

PY - 1999/12/1

Y1 - 1999/12/1

N2 - Various classification algorithms became available due to a surge of interdisciplinary research interests in the areas of data mining and knowledge discovery. We develop a statistical meta-model which compares the classification performances of several algorithms in terms of data characteristics. This empirical model is expected to aid decision making processes of finding the best classification tool in the sense of providing the minimum classification error among alternatives.

AB - Various classification algorithms became available due to a surge of interdisciplinary research interests in the areas of data mining and knowledge discovery. We develop a statistical meta-model which compares the classification performances of several algorithms in terms of data characteristics. This empirical model is expected to aid decision making processes of finding the best classification tool in the sense of providing the minimum classification error among alternatives.

UR - http://www.scopus.com/inward/record.url?scp=0033220832&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033220832&partnerID=8YFLogxK

U2 - 10.1109/34.809107

DO - 10.1109/34.809107

M3 - Article

AN - SCOPUS:0033220832

VL - 21

SP - 1137

EP - 1144

JO - IEEE Transactions on Pattern Analysis and Machine Intelligence

JF - IEEE Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 11

ER -