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 indifferent 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 differentmemory based classifiers for classification of Multivariate data set without having Missing values fromUCI machine learning repository using the open source machine learning tool. A comparison of differentmemory based classifiers used and a practical guideline for selecting the renowned and most suitedalgorithm for a classification is presented. Apart from that some pragmatic criteria for describing andevaluating the best classifiers are discussed.