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Classification of Data at Multilevel Abstraction using Neural Network

Varsha Namdeo, Dr.R. S. Thakur, Dr.G.S. Thakur


Classification is one of the most important tasks in data mining. Researchers are focusing on designing classification algorithms to build accurate and efficient classifiers for large data sets. Classification at multiple levels helps in finding more specific and relevant knowledge.

There are several applications, where it is necessary to classify data at different abstraction level due to sparsity of data. This paper presents an approach for classifying multilevel data from simplified Neural Networks. This work is very useful in the applications where data is spread in multilevel hierarchy and required classification of data at all abstraction levels of data.


Multilevel Classification, Multilevel Abstraction, Artificial Neural Network, Classification by WEKA

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