Decision Trees for Uncertain Data

Introduction to Decision Trees for Uncertain Data:

Decision Trees for Uncertain Data concept work on data which are called as precise. In this paper we cover details about how to cover uncertain information using that data. Examples of uncertain data is data staleness, quantization and measurement errors. In existing method statistical derivatives are used for abstracting  uncertain data but in this paper we explain about using complete information of data item.

For more information on this topic students can download reference material and ppt from below links.

Final year cse and IT students can use Decision Trees for Uncertain Data topic as seminar topic.

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