比较多种机器学习模型在文本分类中的表现，找出最准确的方案。
Performance Analysis of Supervised Machine Learning Algorithms for Text Classification
- 用多种监督学习模型处理不同数据集的文本分类任务。
- 实验显示人工神经网络在准确率上表现最佳。
- 适合需要高效文本分类的工程师或研究人员参考。
文本分类在网络搜索、数据挖掘、网页排名、推荐系统等信息技术领域需求日益增长。本文利用多种标准监督学习技术，在不同数据集上展示文本分类流程。通过标注文本数据进行监督分类，评估各类分类器的性能。研究采用基于反向传播网络的人工神经网络（ANN）模型，结合其他模型构建独立的标注文本分类平台。使用现有基准方法分析分类性能，基于真实数据的实验揭示了各类模型在分类准确率上的表现差异。
原文摘要 · Abstract (English)
The demand for text classification is growing significantly in web searching, data mining, web ranking, recommendation systems, and so many other fields of information and technology. This paper illustrates the text classification process on different datasets using some standard supervised machine learning techniques. Text documents can be classified through various kinds of classifiers. Labeled text documents are used to classify the text in supervised classifications. This paper applies these classifiers on different kinds of labeled documents and measures the accuracy of the classifiers. An Artificial Neural Network (ANN) model using Back Propagation Network (BPN) is used with several other models to create an independent platform for labeled and supervised text classification process. An existing benchmark approach is used to analyze the performance of classification using labeled documents. Experimental analysis on real data reveals which model works well in terms of classification accuracy.
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