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Text classification is the foundation and core of text mining. Naive Bayes is an effective method for text classification. This paper improves the accuracy of Naive Bayes classification using improved information gain, one of methods of feature extraction, by reducing the impact of low-frequency words. In this paper, we use a widely corpus of NLTK. According to the test results, The accuracy of the...
This paper presents our experimental work on machine classification of Nepali texts. We have implemented a Naive Bayes classifier for the task and then augmented it through a multinomial lexicon pooling. The lexicon-pooled Naive Bayes Classifier obtains better results on classification task as compared to a normal Naive Bayes implementation. This hybrid approach also helps in dealing with the unavailability...
Aiming to noise samples in the training dataset, a new method for reducing the amount of training dataset is proposed in the paper which is applicable to text classification. This method describes the distribution of training dataset according to the representativeness score of samples in the class they belong to, so as to show representative samples and noise samples in each class. The new method...
In order to realize the text classification and spam filtering, the Naive Bayesian algorithm estimate what class are the text in by basing on some statistical probability values in accordance with the characteristic in straining sample, but it is easy to expose the overflow problem, this article will optimize the algorithm by setting the threshold, the optimization strategy is comparing the times...
Most conventional incremental learning algorithms perform incremental learning by selecting only one optimized text sample each time, which neither considers the relationship between texts in the unlabeled text set, nor improves incremental learning efficiency. In addition, because of the shortage of the classifierpsilas information storage, the selected optimized text is easily classified incorrectly...
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