International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Enhancement of Random Forest Algorithm Applied to SMS Fraud Detection

Author(s) Justin E. Liwag, Clarisse Anne D. Balaoro
Country Philippines
Abstract This study entitled Enhancement of Random Forest Algorithm Applied to SMS Fraud Detection aims to enhance the algorithm's ability to manage imbalanced datasets and minimize false negatives in classifying fraudulent messages. Random Forest is a powerful machine learning algorithm that builds multiple decision trees from randomly selected subsets of features and data. However, its performance can decline when dealing with imbalanced datasets, often leading to misclassification of the minority class. To address this, Spectral Co-Clustering was integrated to generate cluster-based features, revealing hidden patterns within the data. Initially, text features are transformed into numerical vectors using TF-IDF. To improve data quality, dense rows and columns are retained in the dataset. Furthermore, class weights are adjusted during the training of the Random Forest classifier to mitigate the effects of data imbalance. The results demonstrated a 1% rise in accuracy (from 97% to 98%), a 4% increase in the F1 score for the minority class (from 88% to 92%), and a 6% improvement in recall (from 79% to 85%). Consequently, the findings improved the capability of the enhanced Random Forest classifier in effectively distinguishing between authentic and fraudulent SMS messages, thus providing a cost-effective and efficient approach for boosting the performance of SMS fraud detection systems.
Keywords Random Forest Algorithm, SMS Fraud Detection, Spectral Co-Clustering
Field Computer > Data / Information
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-17
Cite This Enhancement of Random Forest Algorithm Applied to SMS Fraud Detection - Justin E. Liwag, Clarisse Anne D. Balaoro - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32881
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32881
Short DOI https://doi.org/g8wknc
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