Academic Journal
An enhanced algorithm for semantic-based feature reduction in spam filtering
| العنوان: | An enhanced algorithm for semantic-based feature reduction in spam filtering |
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| المؤلفون: | Novo Lourés, María, Pavón Rial, Maria Reyes, Laza Fidalgo, Rosalía, Méndez Reboredo, José Ramón, Ruano Ordás, David Alfonso |
| بيانات النشر: | PeerJ Computer Science Informática Sistemas Informáticos de Nova Xeración |
| سنة النشر: | 2024 |
| المجموعة: | University of Vigo: Investigo (Repositorio Institucional de la Universidade de Vigo) |
| مصطلحات موضوعية: | 1203.17 Informática |
| الوصف: | With the advent and improvement of ontological dictionaries (WordNet, Babelnet), the use of synsets-based text representations is gaining popularity in classification tasks. More recently, ontological dictionaries were used for reducing dimensionality in this kind of representation ( e.g. , Semantic Dimensionality Reduction System (SDRS) (Vélez de Mendizabal et al., 2020)). These approaches are based on the combination of semantically related columns by taking advantage of semantic information extracted from ontological dictionaries. Their main advantage is that they not only eliminate features but can also combine them, minimizing (low-loss) or avoiding (lossless) the loss of information. The most recent (and accurate) techniques included in this group are based on using evolutionary algorithms to find how many features can be grouped to reduce false positive (FP) and false negative (FN) errors obtained. The main limitation of these evolutionary-based schemes is the computational requirements derived from the use of optimization algorithms. The contribution of this study is a new lossless feature reduction scheme exploiting information from ontological dictionaries, which achieves slightly better accuracy (specially in FP errors) than optimization-based approaches but using far fewer computational resources. Instead of using computationally expensive evolutionary algorithms, our proposal determines whether two columns (synsets) can be combined by observing whether the instances included in a dataset ( e.g. , training dataset) containing these synsets are mostly of the same class. The study includes experiments using three datasets and a detailed comparison with two previous optimization-based approaches. ; Xunta de Galicia | Ref. ED431C 2022/03-GRC ; Agencia Estatal de Investigación | Ref. Ref. TIN2017-84658-C2-1-R |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 23765992 |
| Relation: | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84658-C2-1-R/ES; PeerJ Computer Science, 10, e2206 (2024); http://hdl.handle.net/11093/7447; https://peerj.com/articles/cs-2206 |
| DOI: | 10.7717/peerj-cs.2206 |
| الاتاحة: | http://hdl.handle.net/11093/7447 https://doi.org/10.7717/peerj-cs.2206 https://peerj.com/articles/cs-2206 |
| Rights: | Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/ ; openAccess |
| رقم الانضمام: | edsbas.3C873D24 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 23765992 |
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| DOI: | 10.7717/peerj-cs.2206 |