Summarizing Data Sets for Classification

Christopher W. Kinzig; Krishnaprasad Thirunarayan; Gary B. Lamont; Robert E. Marmelstein

Summarizing Data Sets for Classification

Christopher W. Kinzig, Krishnaprasad Thirunarayan, Gary B. Lamont, Robert E. Marmelstein

Wright State University

Research output: Contribution to conference › Presentation

Abstract

This paper describes our approach and experiences with implementing a data mining system using genetic algorithms in C++. In contrast with earlier classification algorithms that tended to “tile” the data sets using some pre-specified “shapes”, the proposed system is based on Marmelstein’s work on determining natural boundaries for class homogeneous regions. These boundaries are further refined to construct a compact set of simple data mining rules for classification.

Original language	American English
State	Published - Jun 1 2001
Event	Proceedings of the International Conference on Artificial Intelligence - Duration: Jun 1 2001 → …

Conference

Conference	Proceedings of the International Conference on Artificial Intelligence
Period	6/1/01 → …

Keywords

Classifier
Data Mining
Genetic Algorithm
Porting
Supervised Learning

Disciplines

Bioinformatics
Communication
Communication Technology and New Media
Computer Sciences
Databases and Information Systems
Life Sciences
OS and Networks
Physical Sciences and Mathematics
Science and Technology Studies
Social and Behavioral Sciences

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Summarizing Data Sets for ClassificationFinal published version, 129 KBLicense: Unspecified

Cite this

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title = "Summarizing Data Sets for Classification",

abstract = " This paper describes our approach and experiences with implementing a data mining system using genetic algorithms in C++. In contrast with earlier classification algorithms that tended to “tile” the data sets using some pre-specified “shapes”, the proposed system is based on Marmelstein{\textquoteright}s work on determining natural boundaries for class homogeneous regions. These boundaries are further refined to construct a compact set of simple data mining rules for classification.",

keywords = "Classifier, Data Mining, Genetic Algorithm, Porting, Supervised Learning",

author = "Kinzig, {Christopher W.} and Krishnaprasad Thirunarayan and Lamont, {Gary B.} and Marmelstein, {Robert E.}",

year = "2001",

month = jun,

day = "1",

language = "American English",

note = "Proceedings of the International Conference on Artificial Intelligence ; Conference date: 01-06-2001",

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TY - CONF

T1 - Summarizing Data Sets for Classification

AU - Kinzig, Christopher W.

AU - Thirunarayan, Krishnaprasad

AU - Lamont, Gary B.

AU - Marmelstein, Robert E.

PY - 2001/6/1

Y1 - 2001/6/1

N2 - This paper describes our approach and experiences with implementing a data mining system using genetic algorithms in C++. In contrast with earlier classification algorithms that tended to “tile” the data sets using some pre-specified “shapes”, the proposed system is based on Marmelstein’s work on determining natural boundaries for class homogeneous regions. These boundaries are further refined to construct a compact set of simple data mining rules for classification.

AB - This paper describes our approach and experiences with implementing a data mining system using genetic algorithms in C++. In contrast with earlier classification algorithms that tended to “tile” the data sets using some pre-specified “shapes”, the proposed system is based on Marmelstein’s work on determining natural boundaries for class homogeneous regions. These boundaries are further refined to construct a compact set of simple data mining rules for classification.

KW - Classifier

KW - Data Mining

KW - Genetic Algorithm

KW - Porting

KW - Supervised Learning

UR - https://corescholar.libraries.wright.edu/knoesis/891

M3 - Presentation

T2 - Proceedings of the International Conference on Artificial Intelligence

Y2 - 1 June 2001

ER -

Summarizing Data Sets for Classification

Abstract

Conference

Keywords

Disciplines

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