Learning premises of fuzzy rules for knowledge acquisition in classification problems

Authors:
N. Xiong;L. Litz;H. Ressom
Affiliations:
Institute of Process Automation, University of Kaiserslautern, Kaiserslautern, Germany;Institute of Process Automation, University of Kaiserslautern, Kaiserslautern, Germany;Department of Electrical and Computer Engineering, University of Maine, Orono, Maine
Venue:
Knowledge and Information Systems
Year:
2002

Citing 12
Cited 4

Genetic-algorithm-based learning

Machine learning
Neural-Network-Based Fuzzy Logic Control and Decision System

IEEE Transactions on Computers - Special issue on artificial neural networks
Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence

Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Distributed representation of fuzzy rules and its application to pattern classification

Fuzzy Sets and Systems
C4.5: programs for machine learning

C4.5: programs for machine learning
Generalizing Version Spaces

Machine Learning
Induction of fuzzy decision trees

Fuzzy Sets and Systems
Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems

Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Generalized Version Space Learning Algorithm for Noisy and Uncertain Data

IEEE Transactions on Knowledge and Data Engineering
Adaptive fuzzy rule-based classification systems

IEEE Transactions on Fuzzy Systems

Rapid development of knowledge-based systems via integrated knowledge acquisition

Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Building classification rules for case-based classifier using fuzzy sets and formal concept analysis

CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
An improved SPSA algorithm for system identification using fuzzy rules for training neural networks

International Journal of Automation and Computing
Fuzzy rule-based similarity model enables learning from small case bases

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

A key issue in building fuzzy classification systems is the specification of rule conditions, which determine the structure of a knowledge base. This paper presents a new approach to automatically extract classification knowledge from numerical data by means of premise learning. A genetic algorithm is employed to search for premise structure in combination with parameters of membership functions of input fuzzy sets to yield optimal conditions of classification rules. The major advantage of our work is that a parsimonious knowledge base with a low number of rules can be achieved. The practical applicability of the proposed method is examined by computer simulations on two well-known benchmark problems of Iris Data and Cancer Data classification.