Selection of evolutionary approach based hybrid data mining algorithms for decision support systems and business intelligence

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.