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Research Paper | Computer Science & Engineering | India | Volume 4 Issue 6, June 2015
A Framework On: Decision Tree for Dynamic Uncertain Data
Megha Pimpalkar | Garima Singh [7]
Abstract: Current research on data stream classification mainly focuses on certain data, in which precise and definite value is usually assumed. However, data with uncertainty is quite natural in real-world application due to various causes, including imprecise measurement, repeated sampling and network errors. In this paper, a new approach is proposed to construct a fuzzy decision tree (FDT) when the training set is built incrementally and when training examples are provided temporally. In this paper, we focus on uncertain data stream classification. Based on DTDU, we propose our DTDU (Decision Tree for Dynamic Uncertain Data) algorithm. Experimental study shows that the proposed DTDU algorithm is efficient in classifying dynamic data stream with uncertain numerical attribute and it is computationally efficient.
Keywords: Uncertain data streams, Decision Tree, Classification, Fuzzy decision tree, Fractional samples
Edition: Volume 4 Issue 6, June 2015,
Pages: 1403 - 1405
How to Cite this Article?
Megha Pimpalkar, Garima Singh, "A Framework On: Decision Tree for Dynamic Uncertain Data", International Journal of Science and Research (IJSR), Volume 4 Issue 6, June 2015, pp. 1403-1405, https://www.ijsr.net/get_abstract.php?paper_id=SUB155563
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