Exploring the Potential of Feature Density in Estimating Machine Learning Classifier Performance with Application to Cyberbullying Detection
Abstrak
In this research, we analyze the potential of Feature Density (FD) 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 af- fecting the environment, as training large-scale ML models are causing alarmingly-growing amounts of COz emissions. Our approach is to optimize the resource-intensive training of ML models for Nat-ural Language Processing to reduce the number of required experiments iterations. We expand on previous attempts on improving classifier training ef-ficiency with FD while also providing an insight to the effectiveness of various linguistically-backed feature preprocessing methods for dialog classifica- tion, specifically cyberbullying detection.
Topik & Kata Kunci
Penulis (6)
J. Eronen
M. Ptaszynski
Fumito Masui
Gniewosz Leliwa
Michal
Wroczynski
Akses Cepat
- Tahun Terbit
- 2022
- Bahasa
- en
- Total Sitasi
- 4×
- Sumber Database
- Semantic Scholar
- DOI
- 10.48550/arXiv.2206.01949
- Akses
- Open Access ✓