Decision Trees for Uncertain Data

Smith Tsang; Wai-Shing Ho; Sau Dan Lee; Ben Kao; Kevin Y. Yip

doi:10.1109/TKDE.2009.175

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2011
Issue No. 1 - January
Abstract - Decision Trees for Uncertain Data

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Decision Trees for Uncertain Data

January 2011 (vol. 23 no. 1)

pp. 64-78

	ASCII Text	x
	Smith Tsang, Ben Kao, Kevin Y. Yip, Wai-Shing Ho, Sau Dan Lee, "Decision Trees for Uncertain Data," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 1, pp. 64-78, January, 2011.

	BibTex	x
	@article{ 10.1109/TKDE.2009.175, author = {Smith Tsang and Ben Kao and Kevin Y. Yip and Wai-Shing Ho and Sau Dan Lee}, title = {Decision Trees for Uncertain Data}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {23}, number = {1}, issn = {1041-4347}, year = {2011}, pages = {64-78}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.175}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - JOUR JO - IEEE Transactions on Knowledge and Data Engineering TI - Decision Trees for Uncertain Data IS - 1 SN - 1041-4347 SP64 EP78 EPD - 64-78 A1 - Smith Tsang, A1 - Ben Kao, A1 - Kevin Y. Yip, A1 - Wai-Shing Ho, A1 - Sau Dan Lee, PY - 2011 KW - Uncertain data KW - decision tree KW - classification KW - data mining. VL - 23 JA - IEEE Transactions on Knowledge and Data Engineering ER -

Smith Tsang, The University of Hong Kong, Hong Kong

Ben Kao, The University of Hong Kong, Hong Kong

Kevin Y. Yip, Yale University, New Haven

Wai-Shing Ho, The University of Hong Kong, Hong Kong

Sau Dan Lee, The University of Hong Kong, Hong Kong

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.175

Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include measurement/quantization errors, data staleness, and multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution. Rather than abstracting uncertain data by statistical derivatives (such as mean and median), we discover that the accuracy of a decision tree classifier can be much improved if the "complete information” of a data item (taking into account the probability density function (pdf)) is utilized. We extend classical decision tree building algorithms to handle data tuples with uncertain values. Extensive experiments have been conducted which show that the resulting classifiers are more accurate than those using value averages. Since processing pdfs is computationally more costly than processing single values (e.g., averages), decision tree construction on uncertain data is more CPU demanding than that for certain data. To tackle this problem, we propose a series of pruning techniques that can greatly improve construction efficiency.

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Index Terms:

Uncertain data, decision tree, classification, data mining.

Citation:

Smith Tsang, Ben Kao, Kevin Y. Yip, Wai-Shing Ho, Sau Dan Lee, "Decision Trees for Uncertain Data," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 1, pp. 64-78, Jan. 2011, doi:10.1109/TKDE.2009.175

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