Classification Techniques in Machine Learning: Applications and Issues

作者：Aized Amin Soofi, Muhammad Arshad Awan · 发表于：Journal of Basic & Applied Sciences · 年份：2017 · DOI：10.6000/1927-5129.2017.13.76 · 被引用次数：316 · 研究领域：Data Stream Mining Techniques、Anomaly Detection Techniques and Applications、Imbalanced Data Classification Techniques

Classification is a data mining (machine learning) technique used to predict group membership for data instances. There are several classification techniques that can be used for classification purpose. In this paper, we present the basic classification techniques. Later we discuss some major types of classification method including Bayesian networks, decision tree induction, k-nearest neighbor classifier and Support Vector Machines (SVM) with their strengths, weaknesses, potential applications and issues with their available solution. The goal of this study is to provide a comprehensive review of different classification techniques in machine learning. This work will be helpful for both academia and new comers in the field of machine learning to further strengthen the basis of classification methods.