基于深度置信网络的维吾尔文垃圾短信分类技术研究

针对传统分类算法对维吾尔文文本分类准确率不高的问题,提出了一种基于深度置信网络的维吾尔文短信文本分类模型。深度学习模拟人脑的多层次结构,对数据从低层到高层逐渐地进行特征提取,深层挖掘数据集的分布规律,从而提高分类准确性。通过逐层无监督的方法完成深度置信网络的初始化,并结合softmax回归分类器实现文本的分类。最后在收集的维吾尔文短信数据集上进行实验论证。实验结果表明,相比KNN、SVM和决策树算法,深度置信网络具有更好的分类效果,准确率更高。

A Uyghur spam classification method based on deep belief networks

Traditional Uygur text classification algorithms have disadvantages such as low accuracy and a long operation time. We therefore propose a Uyghur text messages classification method using the deep learning model. Deep learning simulates the multi-layered structure of the brain which gradually extracts data features from low level to high level, and deeply exploits the distribution law of data sets to improve classification accuracy. We use the layered unsupervised method to initialize the deep belief network, and combining with the softmax regression classifier, we realize the classification of Uyghur message data sets. Experiments on Uyghur messages datasets show that compared with the KNN, SVM and the decision tree algorithm, the proposed method has better classification effect.