NLP
Comparison of AutoML Tools for SMS Spam Message Filtering Including mljar-supervised
- AutoML comparison
- mljar-supervised benchmark
- SMS spam classification
- machine learning text filtering
- ensemble AutoML
- H2O AutoML vs mljar
- TPOT AutoML comparison
- short text classification
- Log Loss AutoML
- AUC spam filtering
MLJAR tools were used in the following publication.
Comparison of Automated Machine Learning Tools for SMS Spam Message Filtering
Waddah Saeed
Center for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, Norway
This study presents a comparative evaluation of three Automated Machine Learning tools — mljar-supervised, H2O AutoML, and TPOT — for SMS spam message filtering. Using a dataset of 5,610 SMS messages and feature subset sizes of 50, 100, and 200, ensemble models consistently achieved the best classification performance. The mljar-supervised Stacked Ensemble model achieved Log Loss as low as 0.8863 and AUC up to 0.9487, demonstrating strong and competitive performance. The work highlights the effectiveness of ensemble-based AutoML pipelines in short-text classification tasks.
arXiv • June 28, 2021
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