一种深度学习的信息文本分类算法
发布时间：2018-11-19 11:22
【摘要】：针对传统文本分类算法准确率低和正确率分布不均匀的问题,提出了基于深度学习的文本分类算法。深度信念网络具有强大的学习能力,可以从高维的原始特征中提取高度可区分的低维特征,不仅能够更全面的考虑到文本信息量,而且能够进行快速分类。采用TF-IDF方法计算文本特征值,利用深度信念网络构造分类器进行精准分类。实验结果表明,与支持向量机、神经网络和极端学习机等常用分类算法相比,该算法有更高的准确率和实用性,为文本的分类研究开拓了新思路。
[Abstract]:Aiming at the problems of low accuracy and uneven distribution of correct rate in traditional text classification algorithms, a text classification algorithm based on depth learning is proposed. Deep belief network has a strong learning ability, it can extract highly distinguishable low-dimensional features from high-dimensional original features, which can not only take into account the amount of text information more comprehensively, but also can be classified quickly. The TF-IDF method is used to calculate the text eigenvalues and the depth belief network is used to construct a classifier for accurate classification. The experimental results show that this algorithm has higher accuracy and practicability than other common classification algorithms such as support vector machine, neural network and extreme learning machine, and opens up a new idea for text classification research.
【作者单位】： 东北林业大学信息与计算机工程学院;
【基金】：中央高校基本科研业务费专项资金(2572015DY07) 黑龙江省自然科学基金(F201347) 哈尔滨市科技创新人才专项资金(2013RFQXJ100) 国家自然科学基金(61300098) 教育部大学生创新训练计划项目(201510225043)
【分类号】：TP391.1
[Abstract]:Aiming at the problems of low accuracy and uneven distribution of correct rate in traditional text classification algorithms, a text classification algorithm based on depth learning is proposed. Deep belief network has a strong learning ability, it can extract highly distinguishable low-dimensional features from high-dimensional original features, which can not only take into account the amount of text information more comprehensively, but also can be classified quickly. The TF-IDF method is used to calculate the text eigenvalues and the depth belief network is used to construct a classifier for accurate classification. The experimental results show that this algorithm has higher accuracy and practicability than other common classification algorithms such as support vector machine, neural network and extreme learning machine, and opens up a new idea for text classification research.
【作者单位】： 东北林业大学信息与计算机工程学院;
【基金】：中央高校基本科研业务费专项资金(2572015DY07) 黑龙江省自然科学基金(F201347) 哈尔滨市科技创新人才专项资金(2013RFQXJ100) 国家自然科学基金(61300098) 教育部大学生创新训练计划项目(201510225043)
【分类号】：TP391.1
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