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Large scale hierarchical classification problem researches how to classify documents into a predefined taxonomy with thousands of categories. As the skewed category distribution over documents, that is, most categories have very few labeled documents, the data sparseness problem in the rare categories lead to a low classification performance. In this paper, we study the problem of web-page classification...
Large scale hierarchical classification problem researches how to classify web documents into the categories among a class hierarchy. As the class hierarchy is very large that containing thousands or even tens of thousands of categories, the performance of the classification is still lower. While a reduce-and-conquer strategy has been proposed to make the problem tractable, candidate search is a bottleneck...
The back propagation(BP) neural network is widely used for text categorization and could achieve high performance. However, the greatest disadvantage of this network is its long training time. The genetic algorithm is often used to generate useful solutions for optimization. In this paper we combined the genetic algorithm and the back propagation neural network for text categorization. We use the...
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