Various Classifiers Performance Based Machine Learning Methods

Abstract

Classification is a form of data mining (regarding machine learning) approach that is helpful in the prediction of group membership for data instances, where the data input is used by the computer program for learning and thereafter this learning is used for classifying the fresh observation made. This data set might just be bi-class or it can be multi-class also. Few instances of the problems in classification include: speech identification, handwriting identification, bio metric detection, document classification etc. Many classification methods exist, which can be utilized for classification. In this research work, the fundamental classification approaches and few important kinds of classification approaches that include decision tree induction, Bayesian networks,k-nearest neighbor classifier and Support Vector Machines (SVM) and fuzzy learning classifiers with their merits, drawbacks, probable applications and challenges faced with the solution available. There are different problems that have an effect on the classification and prediction. The objective of this research work is to render an extensive review of various classification approaches in machine learning. At last, the future work intended on the best classification techniques for the input data are discussed.