In this research. we analyze the potential of Feature Density (HD) as a way to comparatively estimate machine learning (ML) classifier performance prior to training. The goal of the study is to aid in solving the problem of resource-intensive training of ML models which is becoming a serious issue due to continuously increasing dataset sizes and the ever rising popularity of Deep Neural Networks (DNN). The issue of constantly increasing demands for more powerful computational resources is also affecting the environment, as training large-scale ML models are causing alarmingly-growing amounts of CO2, emissions. Our approach 1s to optimize the resource-intensive training of ML models for Natural Language Processing to reduce the number of required experiments iterations. We expand on previous attempts on improving classifier training efficiency with FD while also providing an insight to the effectiveness of various linguistically-backed feature preprocessing methods for dialog classification, specifically cyberbullying detection.

本研究分析了特征密度在机器学习分类器性能比较估计方面的潜力，旨在解决大规模ML模型训练对环境和资源的影响，特别是在自然语言处理中的应用，本文扩展了以前的研究成果，并提出了语言学支持的特征预处理方法以提高分类器的训练效率，具体而言，是在网络欺负检测方面的应用。

探究特征密度在估算机器学习分类器性能中的潜力：以网络欺凌检测为例