Boosted decision graphs for NLP learning tasks
نویسندگان
چکیده
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 1Stage 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.
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