Abstract
Decision Tree is a widely used data classification technique. This paper proposes a decision tree based classification method on uncertain data. Data uncertainty is common in emerging applications, such as sensor networks, moving object databases, medical and biological bases. Data uncertainty can be caused by various factors including measurements precision limitation, outdated sources, sensor errors, network latency and transmission problems. In this paper, we enhance the traditional decision tree algorithms and extend measures, including entropy and information gain, considering the uncertain data interval and probability distribution function. Our algorithm can handle both certain and uncertain datasets. The experiments demonstrate the utility and robustness of the proposed algorithm as well as its satisfactory prediction accuracy.
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Qin, B., Xia, Y., Li, F. (2009). DTU: A Decision Tree for Uncertain Data. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_4
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DOI: https://doi.org/10.1007/978-3-642-01307-2_4
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