Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
A GAs based approach for mining breast cancer pattern
Expert Systems with Applications: An International Journal
Optimal ensemble construction via meta-evolutionary ensembles
Expert Systems with Applications: An International Journal
Constructing response model using ensemble based on feature subset selection
Expert Systems with Applications: An International Journal
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
A hybrid network intrusion detection system using simplified swarm optimization (SSO)
Applied Soft Computing
A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection
Information Sciences: an International Journal
Hi-index | 12.05 |
Data mining usually means the approaches and appliances for the valid new knowledge discovery from databases. A response model can be built as a decision model for prediction or classification of a domain problem potential like expert systems. In this paper, a hybrid meta-evolutionary rule mining based approach to assess numerical data pattern in the classification problems is proposed for extracting the decision rules including the predictors, the corresponding inequalities and parameters simultaneously so as to building a decision-making model with maximum classification accuracy. In real world, problems are highly nonlinear in nature so that it's hard to develop a comprehensive model taking into account all the independent variables through the conventional statistical methods. Recently, nonlinear and complex machine learning approaches such as neural networks and support vector machines have been demonstrated to be with more reliable than the conventional statistical approaches. Although the usefulness of using neural networks and support machines has been reported in literatures, the most obstacles are in model building and use of model in which the classification rules are hard to be realized. Through two numerical experiments, we compared our results against the commercial data mining software and other methods in literature, and then we show experimentally that the proposed approach is promising for improving prediction accuracy and enhancing the modeling simplicity.