Classification of Multivariate Data Sets Without Missing Values Using Memory Based Classifiers - An Effectiveness Evaluation

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Provided by: Academy & Industry Research Collaboration Center
Topic: Data Management
Format: PDF
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 memory-based, tree-based, rule-based, etc are widely used. This paper 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.
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