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| Extracting Text Features with Improved Fruit Fly Optimization Algorithm |
Wen Tingxin1, Li Yangzi1( ), Sun Jingshuang2 |
1Institute of Systems Engineering, Liaoning Technical University, Huludao 125105, China
2 College of Business Administration, Liaoning Technical University, Huludao 125105, China |
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Abstract [Objective] This paper tries to reduce the dimension of text feature vector space and then improves the accuracy of text classification. [Methods] We proposed a text feature selection model IFOATFSO based on the improved fruit fly optimization algorithm. It introduced the classification accuracy variance to monitor the convergence degree of the model. We also used the crossover operator, roulette wheel selection method based on simulated annealing mechanism and genetic algorithm to deepen global search and improve population diversity. [Results] The IFOATFSO model, which optimized the feature selection based on CHI method, not only reduced the feature dimension, but also improved the accuracy of text classification by up to 10.5%. [Limitations] The performance of IFOATFSO model for extracting English text features needs to be improved. [Conclusions] The IFOATFSO model improves the text classification.
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Received: 08 November 2017
Published: 20 June 2018
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