NDLI: Fuzzy Data Mining by Heuristic Rule Extraction and Multiobjective Genetic Rule Selection

Content Provider	IEEE Xplore Digital Library
Author	Ishibuchi, H. Nojima, Y. Kuwajima, I.
Copyright Year	2006
Description	Author affiliation: Osaka Prefecture Univ., Osaka (Ishibuchi, H.; Nojima, Y.; Kuwajima, I.)
Abstract	In this paper, we demonstrate that multiobjective genetic rule selection can significantly improve the accuracy-complexity tradeoff curve of fuzzy rule-based classification systems generated by a heuristic rule extraction procedure for classification problems with many continuous attributes. First a prespecifled number of fuzzy rules are extracted in a heuristic manner based on a rule evaluation criterion. This step can be viewed as fuzzy data mining. Then multiobjective genetic rule selection is applied to the extracted rules to find a number of non-dominated rule sets with respect to accuracy maximization and complexity minimization. This step can be viewed as a postprocessing procedure in fuzzy data mining. Experimental results show that multiobjective genetic rule selection finds a number of smaller rule sets with higher classification accuracy than heuristically extracted rule sets. That is, the accuracy-complexity tradeoff curve of heuristically extracted rule sets in fuzzy data mining is improved by multiobjective genetic rule selection. This observation suggests that multiobjective genetic rule selection plays an important role in fuzzy data mining as a postprocessing procedure.
Starting Page	1633
Ending Page	1640
File Size	388039
Page Count	8
File Format	PDF
ISBN	0780394887
DOI	10.1109/FUZZY.2006.1681926
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Genetics Fuzzy sets Fuzzy systems Backpropagation algorithms Neural networks Machine learning algorithms Evolutionary computation Character generation Radial basis function networks
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Rule extraction from differential evolution trained radial basis function network using genetic algorithms

Effects of heuristic rule generation from multiple patterns in multiobjective fuzzy genetics-based machine learning

Radial basis function based adaptive fuzzy systems

An evolutionary programming based neuro-fuzzy technique for multiobjective generation dispatch

Multi-objective evolutionary design of fuzzy rule-based systems

Optimal Rule Extraction of RBFN Based System Using Hierarchical Self Organised Evolution

Generating single granularity-based fuzzy classification rules for multiobjective genetic fuzzy rule selection

Interactive genetic fuzzy rule selection through evolutionary multiobjective optimization with user preference

Comparison between Fuzzy and Interval Partitions in Evolutionary Multiobjective Design of Rule-Based Classification Systems

Fuzzy Data Mining by Heuristic Rule Extraction and Multiobjective Genetic Rule Selection

Similar Documents

Rule extraction from differential evolution trained radial basis function network using genetic algorithms

Effects of heuristic rule generation from multiple patterns in multiobjective fuzzy genetics-based machine learning

Radial basis function based adaptive fuzzy systems

An evolutionary programming based neuro-fuzzy technique for multiobjective generation dispatch

Multi-objective evolutionary design of fuzzy rule-based systems

Optimal Rule Extraction of RBFN Based System Using Hierarchical Self Organised Evolution

Generating single granularity-based fuzzy classification rules for multiobjective genetic fuzzy rule selection

Interactive genetic fuzzy rule selection through evolutionary multiobjective optimization with user preference

Comparison between Fuzzy and Interval Partitions in Evolutionary Multiobjective Design of Rule-Based Classification Systems

Fuzzy Data Mining by Heuristic Rule Extraction and Multiobjective Genetic Rule Selection