Selection of classification models from repository of model for water quality dataset

This paper proposes a new technique, Model Selection Technique (MST) for selection andranking of models from the repository of models by combining three performance measures(Acc, TPR and TNR). This technique provides weightage to each performance measure to findthe most suitable model from the repository of models. A number of classification modelshave been generated to classify water quality using the most significant features andclassifiers such as J48, JRip and BayesNet. To validate this technique proposed, the waterquality dataset of Kinta River was used in this research. The results demonstrate that theFunction classifier is the optimal model with the most outstanding accuracy of 97.02%, TPR =0.96 and TNR = 0.98. In conclusion, MST is able to find the most relevant model from therepository of models by using weights in classifying the water quality dataset.Keywords: selection of models; water quality; classification model; models repository