What question did this study set out to answer?

This research aims to develop a deep learning model for accurately detecting spam messages in SMS data, particularly under conditions of imbalanced datasets.

May 25, 2023

Deep Learning-based Binary Classification for Spam Detection in SMS Data: Addressing Imbalanced Data with Sampling Techniques

Key Points

This research aims to develop a deep learning model for accurately detecting spam messages in SMS data, particularly under conditions of imbalanced datasets.
Utilized various deep learning models including Dense, LSTM, Bi-LSTM, and GRU for binary classification.
Employed undersampling, downsampling, and SMOTE sampling techniques to address data imbalance.
Analyzed a public dataset of SMS messages from UCL to train and evaluate the models.
The proposed dense model showed high accuracy in detecting spam messages on the test dataset.
The analysis of words used in spam and non-spam messages helped improve model performance.

Abstract

This research paper presents a deep learning-based approach for detecting spam in SMS (text) data. The study uses various models namely Dense, LSTM, Bi-LSTM, and GRU to conduct binary classification and predict spam text messages. To address the imbalanced data problem, the study employs undersampling, downsampling, and SMOTE sampling techniques on a public dataset of SMS messages from UCL datasets. The paper presents a study on detecting spam messages in SMS using a dense model. The researchers visualize the commonly used words in spam and non-spam messages and analyze their impact on the model's performance. The findings from this study demonstrate that the proposed dense model exhibits high accuracy in detecting spam messages on the test dataset. This suggests that the model can be useful in identifying spam messages in SMS.

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