The objective of this research is to enhance performance of Stochastic Gradient Descent (SGD) algorithm in text classification. In our research, we proposed using SGD learning with Grid-Search approach to fine-tuning hyper-parameters in order to enhance the performance of SGD classification. We explored different settings for representation, transformation and weighting features from the summary description of terrorist attacks incidents obtained from the Global Terrorism Database as a pre-classification step, and validated SGD learning on Support Vector Machine (SVM), Logistic Regression and Perceptron classifiers by stratified 10-K-fold cross-validation to compare the performance of different classifiers embedded in SGD algorithm. The research concludes that using a grid-search to find the hyper-parameters optimize SGD classification, not in the pre-classification settings only, but also in the performance of the classifiers in terms of accuracy and execution time.
翻译:研究的目的是提高SGD在文本分类方面的性能。在研究中,我们提议使用SGD学习Gridge-Search方法微调超参数,以提高SGD分类的性能。我们探讨了从全球恐怖主义数据库获得的恐怖袭击事件简要描述中得出的代表、转换和加权特征的不同环境,作为分类前步骤,并验证了SGD在支持矢量机(SVM)、后勤递增和 Perceptron分类师方面的学习,通过分层的10K倍交叉校验来比较SGD算法中所含不同分类员的性能。研究的结论是,利用网格搜索找到超参数优化SGD分类,不仅在分类前环境中,而且在分类员的准确度和执行时间方面进行优化。