Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.
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机译:探究国中补校英文科教学策略 ― 应用折衷式教学法于课堂之行动研究 =An Exploration of English Teaching Strategy at Supplementary Junior High School: Action Research on Applying the Eclectic Method to English Classes
机译:Computer-readable medium, communication terminal, and method for making appropriate selection between promptly receiving communication signal and reducing power consumption
机译:Computer-readable medium, communication terminal, and method for making appropriate selection between promptly receiving communication signal and reducing power consumption