堆叠去噪自编码器在垃圾邮件过滤中的应用

针对垃圾邮件数量日益攀升的问题,提出了将堆叠去噪自编码器应用到垃圾邮件分类中.首先,在无标签数据集上,使用无监督学习方法最小化重构误差,对堆叠去噪自编码器进行贪心逐层预训练,从而获得原始数据更加抽象和健壮的特征表示; 然后,在堆叠去噪自编码器的最上层添加一个分类器后,在有标签数据集上,利用有监督学习方法最小化分类误差,对预训练获得的网络参数进行微调,获得最优化的模型; 最后, 利用训练完成的堆叠去噪编码器在6个不同的公开数据集上进行测试.将准确率、召回率、更具有平衡性的马修斯相关系数作为实验性能评价标准,实验结果表明,相比支持向量机算法、贝叶斯方法和深度置信网络的分类效果,基于堆叠去噪自编码器的垃圾邮件分类器的准确率都高于95%,马修斯相关系数都大于0.88,在应用中具有更高的准确率和更好的健壮性.

Application of stacked denoising autoencoder in spamming filtering

Aiming at the continually increasing number of spams, an approach for spam filtering based on the use of Stacked Denoising Autoencoder (SDA) was proposed. Firstly, to get more abstract and robust feature representation of raw data, greedy layer-wise unsupervised algorithm was used to train the SDA by minimizing the construction error on unlabeled data set. Then a classifier was added on the top level of SDA. Next, the parameters of SDA were optimized with supervised algorithm by minimizing the classification error to obtain a optimal model on labeled data set. Lastly, experiments were performed on six different public corpora using the trained SDA. The performance of SDA algorithm was compared with Support Vector Machine (SVM), Bayes approach and Deep Belief Network (DBN), by using precision, recall, Matthews Correlation Coefficient (MCC) with more balanced performance measure as the experimental measures. The experimental results indicate that using SDA to filter spams has higher precision and more robustness. Since it not only acquires best average performance with all precision greater than 95%, but also gets close to prefect prediction with all MCC greater than 0.88.