Abstract
This paper investigates the impact of various representations of textual input data on the quality of selected classification models in datasets containing specialized vocabulary. By employing various vectorization techniques and machine learning models, the study aims to improve our understanding of the relationship between textual representations and model performance and efficiency.
| Translated title of the contribution | Wpływ różnorodnych, alternatywnych reprezentacji danych wejściowych tekstowych na jakość i wymagania obliczeniowe wybranych modeli klasyfikacyjnych w zbiorach danych zawierających słownictwo specjalistyczne |
|---|---|
| Original language | English |
| Pages (from-to) | 158-166 |
| Number of pages | 9 |
| Journal | Przeglad Elektrotechniczny |
| Volume | 101 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2025 |
Keywords
- Classification Models
- Explainable AI
- Natural Language Processing
- Sustainable AI
- Vectorization Techniques
ASJC Scopus subject areas
- Electrical and Electronic Engineering
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