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An iterative voting method based on word density for text classification

In this paper we present an iterative voting (IV) method using the density based weighting for text classification. An in-class word density is used to weight for each word in a topic, so that the word in documents has an array of weights to vote for given topics, and the highest scored topic will be labeled. During the voting process, the iteration strategy is applied for improving the classification effectiveness. This method shows the competitive performance against SVM, NB, KNN, and it has better time efficiency.