Fuzzy emerging patterns for classifying hard domains

Emerging pattern–based classification is an ongoing branch in Pattern Recognition. However, despite its simplicity and accurate results, this classification includes an a priori discretization step that may degrade the classification accuracy. In this paper, we introduce fuzzy emerging patterns as a...

Full description

Saved in:
Bibliographic Details
Published in:Knowledge and information systems 2011-08, Vol.28 (2), p.473-489
Main Authors: García-Borroto, Milton, Martínez-Trinidad, José Fco, Carrasco-Ochoa, Jesús Ariel
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Applied sciences
Artificial intelligence
Cigars
Classification
Classifiers
Computer Science
Computer science
control theory
systems
Data Mining and Knowledge Discovery
Data processing. List processing. Character string processing
Database Management
Discretization
Exact sciences and technology
Fuzzy
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Information Storage and Retrieval
Information systems
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Memory organisation. Data processing
Mining
Neighborhoods
Pattern recognition
Regular Paper
Semantics
Software
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Emerging pattern–based classification is an ongoing branch in Pattern Recognition. However, despite its simplicity and accurate results, this classification includes an a priori discretization step that may degrade the classification accuracy. In this paper, we introduce fuzzy emerging patterns as an extension of emerging patterns to deal with numerical attributes using fuzzy discretization. Based on fuzzy emerging patterns, we propose a new classifier that uses a novel graph organization of patterns. The new classifier outperforms some popular and state of the art classifiers on several UCI repository databases. In a pairwise comparison, it significantly beats every other single classifier.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-010-0324-x