Boosted decision graphs for NLP learning tasks
Jon David Patrick, Ishaan Goyal · 2001
This paper reports the implementation of DRAPH-GP an extension of the decision graph algorithm DGRAPH-OW using the AdaBoost algorithm. This algorithm, which we call 1-Stage Boosting, is shown to improve the accuracy of decision graphs, along with another technique which we combine with AdaBoost and call 2-Stage Boosting which shows greater improvement. Empirical tests demonstrate that both 1-Stage and 2-Stage Boosting techniques perform better than the boosted C4.5 algorithm (C5.0). The boosting has shown itself competitive for NLP tasks with a high disjunction of attribute space against memory based methods, and potentially better if part of an Hierarchical Multi-Method Classifier. An explanation for the effectiveness of boosting due to a poor choice of prior probabilities is presented.