A Genetic Integrated Fuzzy Classifier

Vito Di Gesu'; Giosue' Lo Bosco

A Genetic Integrated Fuzzy Classifier

Vito Di Gesu', Giosue' Lo Bosco

Matematica e Informatica

Risultato della ricerca: Article › peer review

7 Citazioni (Scopus)

Abstract

This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells.The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.

Lingua originale	English
pagine (da-a)	411-420
Numero di pagine	10
Rivista	Pattern Recognition Letters
Volume	26
Stato di pubblicazione	Published - 2005

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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title = "A Genetic Integrated Fuzzy Classifier",

abstract = "This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells.The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.",

keywords = "Classification, Classifier Ensemble, Evolutionary Algorithms., Classification, Classifier Ensemble, Evolutionary Algorithms.",

author = "{Di Gesu'}, Vito and {Lo Bosco}, Giosue'",

year = "2005",

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AU - Di Gesu', Vito

AU - Lo Bosco, Giosue'

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AB - This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells.The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.

KW - Classification

KW - Classifier Ensemble

KW - Evolutionary Algorithms.

KW - Classification

KW - Classifier Ensemble

KW - Evolutionary Algorithms.

UR - http://hdl.handle.net/10447/40545

M3 - Article

VL - 26

SP - 411

EP - 420

JO - Pattern Recognition Letters

JF - Pattern Recognition Letters

SN - 0167-8655

ER -