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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction
verfasst von : Ted Gueniche, Philippe Fournier-Viger, Vincent S. Tseng
Erschienen in: Advanced Data Mining and Applications
Verlag: Springer Berlin Heidelberg
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Predicting the next item of a sequence over a finite alphabet has important applications in many domains. In this paper, we present a novel prediction model named CPT (
C
ompact
P
rediction
T
ree) which losslessly compress the training data so that all relevant information is available for each prediction. Our approach is incremental, offers a low time complexity for its training phase and is easily adaptable for different applications and contexts. We compared the performance of CPT with state of the art techniques, namely PPM (
P
rediction by
P
artial
M
atching), DG (
D
ependency
G
raph) and All-
K
-th-Order Markov. Results show that CPT yield higher accuracy on most datasets (up to 12% more than the second best approach), has better training time than DG and PPM, and is considerably smaller than All-
K
-th-Order Markov.