Full Program
Summary:
Short Message Service (SMS) is a widely used text messaging feature available on both basic and smartphones making SMS spam detection a critical task. Supervised machine learning approaches often face challenges in this domain due to their dependence on manually crafted features such as keyword detection which can result in simplistic patterns and misclassification of more complex messages. Furthermore these models can exacerbate human-induced bias if the training data include inconsistent labeling or subjective interpretations leading to unfair treatment of specific keywords or contexts.We propose a Context-Enhanced Clustering (CEC) approach to address these challenges by leveraging contextual metadata adaptive thresholding and modified similarity measures for clustering. We evaluate our approach using the English SMS spam dataset source from UC Irvine’s Machine Learning Repository. CEC identifies representative samples from the SMS dataset to fine-tune LLMs such as ChatGPT-4 improving the robustness and fairness of spam classification. Our approach outperforms traditional clustering
Author(s):
Gerard Shu Fuhnwi
Montana State University
United States
Ann Marie Reinhold
Montana State University, Pacific Northwest National Laboratory
United States
Clemente Izurieta
Montana State University, Pacific Northwest National Laboratory and Idaho National Laboratory,
United States