[论文解读] Text Classification Using Hybrid Machine Learning Algorithms on Big Data

Doris C. Asogwa, S. O. Anigbogu+2 more|arXiv (Cornell University)|Mar 30, 2021

Spam and Phishing Detection参考文献 10被引用 12

一句话总结

本文提出了一种结合朴素贝叶斯和支撑向量机（SVM）的混合机器学习模型，并结合文本挖掘技术，以提升在大数据上的文本分类准确率与效率。该方法降低了计算复杂度，在使用WEKA和Java处理社交媒体文本数据集时，实现了96.76%的准确率，显著优于独立的朴素贝叶斯（61.45%）和SVM（69.21%）。

ABSTRACT

Recently, there are unprecedented data growth originating from different online platforms which contribute to big data in terms of volume, velocity, variety and veracity (4Vs). Given this nature of big data which is unstructured, performing analytics to extract meaningful information is currently a great challenge to big data analytics. Collecting and analyzing unstructured textual data allows decision makers to study the escalation of comments/posts on our social media platforms. Hence, there is need for automatic big data analysis to overcome the noise and the non-reliability of these unstructured dataset from the digital media platforms. However, current machine learning algorithms used are performance driven focusing on the classification/prediction accuracy based on known properties learned from the training samples. With the learning task in a large dataset, most machine learning models are known to require high computational cost which eventually leads to computational complexity. In this work, two supervised machine learning algorithms are combined with text mining techniques to produce a hybrid model which consists of Naïve Bayes and support vector machines (SVM). This is to increase the efficiency and accuracy of the results obtained and also to reduce the computational cost and complexity. The system also provides an open platform where a group of persons with a common interest can share their comments/messages and these comments classified automatically as legal or illegal. This improves the quality of conversation among users. The hybrid model was developed using WEKA tools and Java programming language. The result shows that the hybrid model gave 96.76% accuracy as against the 61.45% and 69.21% of the Naïve Bayes and SVM models respectively.

研究动机与目标

• 解决来自社交媒体平台等大数据源的非结构化、高速文本数据分析挑战。
• 降低大规模数据集上文本分类任务的计算复杂度与成本。
• 通过结合朴素贝叶斯与SVM，提升分类准确率，超越单一机器学习模型的表现。
• 开发一个开放平台，用于将用户评论分类为合法或非法，从而提升对话质量。

提出的方法

• 通过整合朴素贝叶斯与支持向量机（SVM）分类器，构建混合模型用于文本分类。
• 应用文本挖掘技术对原始非结构化文本数据进行预处理并提取特征。
• 使用WEKA机器学习工具包和Java编程语言实现该模型。
• 在社交媒体文本数据集上进行训练与测试，以评估性能。
• 混合方法利用两种算法的优势：朴素贝叶斯用于概率分类，SVM用于高维特征空间。
• 以准确率为首要指标，对比混合模型与单一模型的性能。

实验结果

研究问题

• RQ1 与单独使用任一模型相比，结合朴素贝叶斯与SVM是否能提升大数据上的文本分类准确率？
• RQ2 该混合模型在大规模文本分类中如何降低计算复杂度与成本？
• RQ3 该混合模型在多大程度上能有效将社交媒体平台上的用户生成内容分类为合法或非法？
• RQ4 文本挖掘技术的集成是否提升了特征表示与分类性能？

主要发现

• 混合模型实现了96.76%的分类准确率，显著优于独立的朴素贝叶斯（61.45%）和SVM（69.21%）。
• 该混合方法通过利用两种算法的互补优势，降低了计算复杂度。
• 文本挖掘技术的集成改善了特征提取与模型输入质量。
• 该系统提供了一个可扩展的开放平台，用于社交媒体用户评论的自动化分类。
• 结果表明，混合模型能够有效应对大数据的4V特征——体量（volume）、速度（velocity）、多样性（variety）与真实性（veracity），同时保持高准确率。
• 本研究证实，将概率模型与基于核的方法结合，可显著提升在非结构化数据上的文本分类任务性能。