What is it about?
Data Stream occurs continuously and fast. Many incremental learners are intended to deal with this problem. This article will help you to design a classifier for evolving data streams using Neural Network. This classifier is updated using rough set theory and holoentropy. We have evaluated performance using Accuracy, Precision, Recall, Computational time.
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Why is it important?
This article will help you to design a classifier for evolving data streams using Neural Network. This classifier is updated using rough set theory and holoentropy. We have evaluated performance using Accuracy, Precision, Recall, Computational time.
Perspectives
Data classification becomes a main task in the data stream mining field. Data Concept can develop with time, called as concept drift. Due to concept drift storage of data streams in main memory is not possible. In such environment storage, querying and mining become a difficult task. We deal with this and more data stream classification issues. We designed a classifier which can handle this issues easily.
Dr Jagannath E Nalavade
Vel Tech High Tech Dr Rangarajan Dr Sakunthala Engineering College
Read the Original
This page is a summary of: HRNeuro-fuzzy: Adapting neuro-fuzzy classifier for recurring concept drift of evolving data streams using rough set theory and holoentropy, Journal of King Saud University - Computer and Information Sciences, November 2016, Elsevier,
DOI: 10.1016/j.jksuci.2016.11.005.
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