Abstract
In engineering practice, we need to consult a large number of professional data to support theoretical innovation. If we rely on manual classification and screening one by one, it will take a lot of time and energy, and the classification accuracy can not meet the requirements. In order to improve the work efficiency, a multi-level feature selection algorithm based on MapReduce is proposed. The improved Chi feature selection algorithm can be used for the initial screening, and then the noise words and pre quality features can be filtered by mutual information method. The experimental results show that the algorithm not only ensures the low time complexity of processing big data, but also improves the accuracy of text classification.
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