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Boosted Decision Graphs for NLP Learning Tasks

Jon D. Patrick and Ishaan Goyal

This paper reports the implementation of the AdaBoost algorithm on decision graphs, optimized using the Minimum Message Length Principle. The AdaBoost algorithm, which we call 1-Stage Boosting, is shown to improve the accuracy of decision graphs, along with we another technique which we combine with AdaBoost and call 2-Stage Boosting. which shows the greater improvement. Empirical tests demonstrate that both 1-Stage and 2-Stage Boosting techniques perform better than the boosted C4.5 algorithm. However the boosting has not shown a significant improvement for NLP tasks with a high disjunction of attribute space.

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Last update: July 12, 2001.