Combining Active Learning and Boosting for Naïve Bayes Text Classifiers
نویسندگان
چکیده
This paper presents a variant of the AdaBoost algorithm for boosting Näıve Bayes text classifier, called AdaBUS, which combines active learning with boosting algorithm. Boosting has been evaluated to effectively improve the accuracy of machine-learning based classifiers. However, Näıve Bayes classifier, which is remarkably successful in practice for text classification problems, is known not to work well with the boosting technique due to its instability of base classifiers. The proposed algorithm focuses on boosting Näıve Bayes classifiers by performing active learning at each iteration of boosting process. The basic idea is to induce perturbation of base classifiers by augmenting the training set with the most informative unlabeled documents.
منابع مشابه
A Method to Boost Naïve Bayesian Classifiers
In this paper, we introduce a new method to improve the performance of combining boosting and naïve Bayesian. Instead of combining boosting and Naïve Bayesian learning directly, which was proved to be unsatisfactory to improve performance, we select the training samples dynamically by bootstrap method for the construction of naïve Bayesian classifiers, and hence generate very different or unsta...
متن کاملClassification Using Naïve Bayes- a Survey
Classification, particularly Text Classification, is a supervised learning approach categorizing into various categories, the available training set of correctly identified observations analyzed into a set of features. There are many phases involved in classification. The main classification phase involves the use of classification algorithms or classifiers. Among the various classifiers, the N...
متن کاملInterpretable Boosted Naïve Bayes Classification
Voting methods such as boosting and bagging provide substantial improvements in classification performance in many problem domains. However, the resulting predictions can prove inscrutable to end-users. This is especially problematic in domains such as medicine, where end-user acceptance often depends on the ability of a classifier to explain its reasoning. Here we propose a variant of the boos...
متن کاملAn Expert Cognitive System Using Ada-boost Algorithm
Recent works on ensemble methods like Adaptive Boosting have been applied successfully in many problems. Ada-Boost algorithm running on a given weak learner several times on slightly altered data and combining the hypotheses in order to achieve higher accuracy than the weak learner. This paper presents an expert system that boosts the performance of an ensemble of classifiers. In, Boosting, a s...
متن کاملBoosting the Tree Augmented Naïve Bayes Classifier
The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the per...
متن کامل