Decision Tree for Dynamic and Uncertain Data Streams

Chunquan Liang, Yang Zhang, Qun Song
Proceedings of 2nd Asian Conference on Machine Learning, PMLR 13:209-224, 2010.

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, we focus on uncertain data stream classification. Based on CVFDT and DTU, we propose our UCVFDT (Uncertainty-handling and Concept-adapting Very Fast Decision Tree) algorithm, which not only maintains the ability of CVFDT to cope with concept drift with high speed, but also adds the ability to handle data with uncertain attribute. Experimental study shows that the proposed UCVFDT algorithm is efficient in classifying dynamic data stream with uncertain numerical attribute and it is computationally efficient.

Cite this Paper

BibTeX
@InProceedings{pmlr-v13-liang10a, title = {Decision Tree for Dynamic and Uncertain Data Streams}, author = {Liang, Chunquan and Zhang, Yang and Song, Qun}, booktitle = {Proceedings of 2nd Asian Conference on Machine Learning}, pages = {209--224}, year = {2010}, editor = {Sugiyama, Masashi and Yang, Qiang}, volume = {13}, series = {Proceedings of Machine Learning Research}, address = {Tokyo, Japan}, month = {08--10 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v13/liang10a/liang10a.pdf}, url = {https://proceedings.mlr.press/v13/liang10a.html}, 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, we focus on uncertain data stream classification. Based on CVFDT and DTU, we propose our UCVFDT (Uncertainty-handling and Concept-adapting Very Fast Decision Tree) algorithm, which not only maintains the ability of CVFDT to cope with concept drift with high speed, but also adds the ability to handle data with uncertain attribute. Experimental study shows that the proposed UCVFDT algorithm is efficient in classifying dynamic data stream with uncertain numerical attribute and it is computationally efficient.} }
Endnote
%0 Conference Paper %T Decision Tree for Dynamic and Uncertain Data Streams %A Chunquan Liang %A Yang Zhang %A Qun Song %B Proceedings of 2nd Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2010 %E Masashi Sugiyama %E Qiang Yang %F pmlr-v13-liang10a %I PMLR %P 209--224 %U https://proceedings.mlr.press/v13/liang10a.html %V 13 %X 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, we focus on uncertain data stream classification. Based on CVFDT and DTU, we propose our UCVFDT (Uncertainty-handling and Concept-adapting Very Fast Decision Tree) algorithm, which not only maintains the ability of CVFDT to cope with concept drift with high speed, but also adds the ability to handle data with uncertain attribute. Experimental study shows that the proposed UCVFDT algorithm is efficient in classifying dynamic data stream with uncertain numerical attribute and it is computationally efficient.
RIS
TY - CPAPER TI - Decision Tree for Dynamic and Uncertain Data Streams AU - Chunquan Liang AU - Yang Zhang AU - Qun Song BT - Proceedings of 2nd Asian Conference on Machine Learning DA - 2010/10/31 ED - Masashi Sugiyama ED - Qiang Yang ID - pmlr-v13-liang10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 13 SP - 209 EP - 224 L1 - http://proceedings.mlr.press/v13/liang10a/liang10a.pdf UR - https://proceedings.mlr.press/v13/liang10a.html AB - 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, we focus on uncertain data stream classification. Based on CVFDT and DTU, we propose our UCVFDT (Uncertainty-handling and Concept-adapting Very Fast Decision Tree) algorithm, which not only maintains the ability of CVFDT to cope with concept drift with high speed, but also adds the ability to handle data with uncertain attribute. Experimental study shows that the proposed UCVFDT algorithm is efficient in classifying dynamic data stream with uncertain numerical attribute and it is computationally efficient. ER -
APA
Liang, C., Zhang, Y. & Song, Q.. (2010). Decision Tree for Dynamic and Uncertain Data Streams. Proceedings of 2nd Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 13:209-224 Available from https://proceedings.mlr.press/v13/liang10a.html.

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