Performance Evaluation of Evolutionary and Artificial Neural Network Based Classifiers in Diversity of Datasets
In the last two decades, we have seen an explosive growth in our capabilities to both generate and collect data. Advances in scientific data collections (e.g. from remote sensors or from space satellites), the widespread use of bar codes for almost all commercial products, and the computerization of many business and government transactions have generated a sea of data. So there is a need for automatic tools and techniques for such a huge collection of data. These techniques and tools are the subject of the emerging field of knowledge discovery in databases (KDD) and data mining. Data mining plays an important role to discover important information to help in decision making of a decision support system. It has been the active area of research in the last decade. The classification is one of the important tasks of data mining. Different kind of classifiers have been suggested and tested to predict the future events based on unseen data. This paper compares the performance evaluation of evolutionary based genetic algorithm and artificial neural network based classifiers in diversity of datasets. The performance evaluation metrics are predictive accuracy, training time and comprehensibility. Evolutionary based classifier shows better comprehensibility and predictive accuracy as compared to ANN based classifier. Such a classifier is slower as compared to the ANN based one.
KeywordsKnowledge Discovery in Databases Evolutionary Computation Artificial Neural Network STAGLOG
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