AN AUTONOMOUS FRAMEWORK FOR CLASSIFIER SELECTION IN WEKA (International Journal of Engineering and Computer Science, 2017)
Automating Classifier selection is an interesting task in Data Mining. In this paper we describe a framework for automatic Classifier selection integrated into Weka for identifying a model for effective classification for user’s dataset. With this Framework we can easily achieve the expected result at extremely fast learning speed. It automatically selects the parameters based on algorithms present in Weka and determines the best model for the input dataset. Also the lists of available classifiers are open to the users, allowing them to choose depending on the dataset.