Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
Democracy in neural nets: voting schemes for classification
Neural Networks
Fast discovery of association rules
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Neural Network Training Using Genetic Algorithms
Neural Network Training Using Genetic Algorithms
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Mining fuzzy association rules for classification problems
Computers and Industrial Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Fuzzy Data Mining: Effect of Fuzzy Discretization
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fuzzy query translation for relational database systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy control of pH using genetic algorithms
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
Extracting Fuzzy Linguistic Summaries Based on Including Degree Theory and FCA
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Mining associative classification rules with stock trading data - A GA-based method
Knowledge-Based Systems
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
A fuzzy integral fusion approach in analyzing competitiveness patterns from WCY2010
Knowledge-Based Systems
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This paper propose a new method, that employs the genetic algorithm, to find fuzzy association rules for classification problems based on an effective method for discovering the fuzzy association rules, namely the fuzzy grids based rules mining algorithm (FGBRMA). It is considered that some important parameters, including the number and shapes of membership functions in each quantitative attribute and the minimum fuzzy support, are not easily user-specified. Thus, the above-mentioned parameters are automatically determined by a binary string or chromosome is composed of two substrings: one for each quantitative attribute by the coding method proposed by Ishibuchi and Murata, and the other for the minimum fuzzy support. In each generation, the fitness value, which maximizes the classification accuracy rate and minimizes the number of fuzzy rules, of each chromosome can be obtained. When reaching the termination condition, a chromosome with maximum fitness value is then used to test its performance. For classification generalization ability, the simulation results from the iris data and the appendicitis data demonstrate that proposed method performs well in comparison with other classification methods.