CLASSIFICATION OF MULTIVARIATE DATA SETS WITHOUT MISSING VALUES USING MEMORY BASED CLASSIFIERS - AN EFFECTIVENESS EVALUATION

Classification is a gradual practice for allocating a given piece of input into any of the known category. Classification is a crucial Machine Learning technique. There are many classification problem occurs in different application areas and need to be solved. Different types are classification algorithms like memorybased, tree-based, rule-based, etc are widely used. This work evaluates the performance of different memory based classifiers for classification of Multivariate data set without having Missing values from UCI machine learning repository using the open source machine learning tool. A comparison of different memory based classifiers used and a practical guideline for selecting the renowned and most suited algorithm for a classification is presented. Apart from that some pragmatic criteria for describing and evaluating the best classifiers are discussed.