Performance Evaluation of Predictive Classifiers for Knowledge Discovery from Engineering Materials Data Sets

In this paper, naive Bayesian and C4.5 Decision Tree Classifiers (DTC) are successively applied in materials informatics to classify the engineering materials into different classes for the selection of materials that suit the input design specifications. Here, the classifiers are analyzed individually and their performance evaluation is analyzed with confusion matrix predictive parameters and standard measures, the classification results are analyzed on different class of materials. Comparison of classifiers has found that naive Bayesian classifier is more accurate and better than the C4.5 DTC. The knowledge discovered by the naive Bayesian classifier can be employed for decision making in materials selection in manufacturing industries.