Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Combining GP operators with SA search to evolve fuzzy rule based classifiers
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Discovery of Decision Rules from Databases: An Evolutionary Approach
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Multivariate Versus Univariate Decision Trees
Multivariate Versus Univariate Decision Trees
A Comparison of Decision Tree Classifiers with Backpropagation Neural Networks for Multi-Modal Classification Problems
A survey of evolutionary algorithms for data mining and knowledge discovery
Advances in evolutionary computing
Simplifying decision trees: A survey
The Knowledge Engineering Review
Classifier fitness based on accuracy
Evolutionary Computation
Evolutionary product-unit neural networks classifiers
Neurocomputing
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
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The business intelligence is constantly changing, and it is becoming more complex. Organizations, private and public, are under pressures that force them to respond quickly to changing conditions and to be innovative in the way they operate. Such activities require organizations to be agile and to make frequent and quick strategic, tactical, and operational decisions. In today's era, we observe major changes in how managers use computerized support in making decisions. As more number of decision-makers become computer literate, decision support systems (DSS) is evolving from its beginning as a personal support tool and is becoming the shared resource in an organization. Data mining has been an active area of research in last two decades. Integration of data mining and decision support systems (DSS) can lead to the improved performance and can enable the tackling of new types of problems. In the recent past, there has been an increasing interest in applying evolutionary methods to Knowledge Discovery in Databases (KDD) and a number of successful applications of Genetic Algorithms (GA) and Genetic Programming (GP) to KDD have been demonstrated. No single algorithm has been found to be superior over all others for all data sets. This paper sheds some light on the selection of evolutionary approach based hybrid classification models in diversity of datasets from different domains. NNEP-C (s), XCS-C (s) GFS-GP-C(s) evolved from the combination of genetic algorithm and other techniques as neural network, self evolving GA and fuzzy learning have been tested on five datasets based on selected quality measures like predictive accuracy and training time. XCS-C(s) shows faster speed as compared to its competitor NNEP-C(s). GFS-GP-C(s) is the slowest one.