Fuzzy classification using the data envelopment analysis

Parag Pendharkar

doi:10.1016/j.knosys.2012.03.007

Fuzzy classification using the data envelopment analysis

Parag Pendharkar

School of Business Administration (Harrisburg)

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

We develop a fuzzy classification system using data envelopment analysis (DEA) and illustrate its application using a simple graduate admissions decision-making problem. Using simulated and real-world datasets, we benchmark the proposed DEA based fuzzy classification system (DBFCS) with the adaptive neuro fuzzy inference system (ANFIS), fuzzy rule based classification system (FRCS) and logistic regression (Logit); and illustrate that the DBFCS outperforms all competing models.

Original language	English (US)
Pages (from-to)	183-192
Number of pages	10
Journal	Knowledge-Based Systems
Volume	31
DOIs	https://doi.org/10.1016/j.knosys.2012.03.007
State	Published - Jul 1 2012

All Science Journal Classification (ASJC) codes

Management Information Systems
Software
Information Systems and Management
Artificial Intelligence

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10.1016/j.knosys.2012.03.007

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abstract = "We develop a fuzzy classification system using data envelopment analysis (DEA) and illustrate its application using a simple graduate admissions decision-making problem. Using simulated and real-world datasets, we benchmark the proposed DEA based fuzzy classification system (DBFCS) with the adaptive neuro fuzzy inference system (ANFIS), fuzzy rule based classification system (FRCS) and logistic regression (Logit); and illustrate that the DBFCS outperforms all competing models.",

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AB - We develop a fuzzy classification system using data envelopment analysis (DEA) and illustrate its application using a simple graduate admissions decision-making problem. Using simulated and real-world datasets, we benchmark the proposed DEA based fuzzy classification system (DBFCS) with the adaptive neuro fuzzy inference system (ANFIS), fuzzy rule based classification system (FRCS) and logistic regression (Logit); and illustrate that the DBFCS outperforms all competing models.

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VL - 31

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JO - Knowledge-Based Systems

JF - Knowledge-Based Systems

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Fuzzy classification using the data envelopment analysis

Abstract

All Science Journal Classification (ASJC) codes

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