| Download | - View final version: Text augmentation using a graph-based approach and clonal selection algorithm (PDF, 1.1 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1016/j.mlwa.2023.100452 |
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| Author | Search for: Ahmed, HadeerORCID identifier: https://orcid.org/0000-0001-9568-1563; Search for: Traore, Issa; Search for: Mamun, Mohammad1; Search for: Saad, Sherif |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Subject | data augmentation; unstructured data; cybersecurity; text generation; clonal selection |
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| 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. |
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| Publication date | 2023-01-23 |
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| Publisher | Elsevier BV |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Identifier | S2666827023000051 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 4a4024e3-9a0e-455d-be04-6da554a4cc29 |
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| Record created | 2023-02-03 |
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| Record modified | 2023-02-08 |
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