Text augmentation using a graph-based approach and clonal selection algorithm
Date
2023
Authors
Ahmed, Hadeer
Traore, Issa
Mamun, Mohammad
Saad, Sherif
Journal Title
Journal ISSN
Volume Title
Publisher
Machine Learning with Applications
Abstract
Annotated data is critical for machine learning models, but producing large amounts of data with high-quality labeling is a time-consuming and labor-intensive process. Natural language processing (NLP) and machine learning models have traditionally relied on the labels given by human annotators with varying degrees of competency, training, and experience. These kinds of labels are incredibly problematic because they are defined and enforced by arbitrary and ambiguous standards. In order to solve these issues of insufficient high-quality labels, researchers are now investigating automated methods for enhancing training and testing data sets. In this paper, we demonstrate how our proposed method improves the quality and quantity of data in two cybersecurity problems (fake news identification & sensitive data leak) by employing the clonal selection algorithm (CLONALG) and abstract meaning representation (AMR) graphs, and how it improves the performance of a classifier by at least 5% on two datasets.
Description
Keywords
Data augmentation, Unstructured data, Cybersecurity, Text generation, Clonal selection
Citation
Ahmed, H., Traore, I., Mamun, M., Saad, S. (2023). Text augmentation using a graph-based approach and clonal selection algorithm. Machine Learning with Applications, 11, 100452. https://doi.org/10.1016/j.mlwa.2023.100452.