用改进的TF-IDF和快速降维算法，让传统模型更省电更快。
An Efficient Classification Model for Cyber Text
- 提出CTF-IDF改进文本特征提取，提升信息密度。
- 结合IRLBA降维，使模型训练时间大幅缩短。
- 适合追求低功耗、快响应的文本分类场景。
近年来深度学习迅猛发展，但其对计算资源和电力的高需求带来了严重的碳足迹问题。文本分析领域也深受这一趋势影响。本文提出了一种改进的TF-IDF算法——克莱门特词频-逆文档频率（CTF-IDF），用于数据预处理，并结合一种快速的IRLBA算法进行降维。将这两项技术引入传统文本分析流程，相比深度学习方法，在显著降低计算开销与碳排放的同时，仅以轻微精度损失为代价，实现了更高效、更快的分类应用。实验结果表明，所提方法在时间复杂度上实现数量级降低，且模型准确率进一步提升。
原文摘要 · Abstract (English)
The uprising of deep learning methodology and practice in recent years has brought about a severe consequence of increasing carbon footprint due to the insatiable demand for computational resources and power. The field of text analytics also experienced a massive transformation in this trend of monopolizing methodology. In this paper, the original TF-IDF algorithm has been modified, and Clement Term Frequency-Inverse Document Frequency (CTF-IDF) has been proposed for data preprocessing. This paper primarily discusses the effectiveness of classical machine learning techniques in text analytics with CTF-IDF and a faster IRLBA algorithm for dimensionality reduction. The introduction of both of these techniques in the conventional text analytics pipeline ensures a more efficient, faster, and less computationally intensive application when compared with deep learning methodology regarding carbon footprint, with minor compromise in accuracy. The experimental results also exhibit a manifold of reduction in time complexity and improvement of model accuracy for the classical machine learning methods discussed further in this paper.
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