A case study on machine learning and classification

As a young research field, the machine learning has made significant progress and covered a broad spectrum of applications for the last few decades. Classification is an important task of machine learning. Today, the task is used in a vast array of areas. The present article provides a case study on various classification algorithms (under machine learning), their applicability and issues. More specifically, a step by step progress on this area is discussed in this paper. Further, an experiment is conducted over 12 real-world datasets drawn from University of California, Irvine (UCI, a machine learning repository) using four competent individual learners namely, C4.5 (decision tree-based classifier), Naive Bayes, k-nearest neighbours (k-NN), neural network and two hybrid learners: Bagging (based on decision tree) and (fuzzy + rough-set + k-NN: a hybrid system) for head to head comparison of their classification performance. Their merits and demerits (as discussed in this article) are analysed accordingly with the obtained results.