基于深度神经网络和模糊规则的文本分类方法

Research of text classification method based on deep neural network and fuzzy rule

传统的单个模糊分类器方法采用固定的去模糊化规则,在情感数据分类上容易引起文本歧义,针对该问题,提出一种基于深度神经网络和模糊规则的文本分类方法。该方法分为两个主要阶段。第1个阶段,利用模糊规则形成算法与不同的模糊范数,利用特征提取方法(词袋法、词嵌入向量),以及基于关联的特征子集选择方法来制备特征,从而训练多个模糊分类器;然后,进行分类器融合以识别歧义实例。第2个阶段,整理歧义实例,生成第2个训练集,使用KNN对新出现的歧义实例进行分类。与当前已有的先进方法相比,所提方法在大部分情况下具有更优的分类性能,Wilcoxon秩检验的统计具有显著性。

The traditional single fuzzy classifier method, which uses fixed de-fuzzification rules, is easy to case the problem of text ambiguity in emotional data. To solve the problem, a text classification method based on deep neural networks and fuzzy rules is proposed. The method is divided into two main stages. In the first stage, we use fuzzy rule formation algorithm and different fuzzy norms, feature extraction method(word bag method, word embedding vector), and feature subset selection method based on association to prepare features, so as to train multiple fuzzy classifiers. Then, we fuse classifiers to identify ambiguous instances. In the second stage, disambiguation instances are sorted out, a second training set is generated, and KNN is used to classify new disambiguation instances. Compared with the current advanced methods, the proposed method has better classification performance in most cases, and the Wilcoxon rank test is statistically significant.