An Evolutionary-based Random Weight Networks with Taguchi Method for Arabic Web Pages Classification
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Summary
An evolutionary model based on binary particle swarm optimization (BPSO) combined with random weight networks (RWNs) as an induction algorithm to reduce the high dimensionality of features in the Arabic web pages and to perform document classification automatically is proposed.
- Type
- article
- Published
- 2021-02-05
- Cited by
- 5
- References
- 92
- OpenAlex
- https://openalex.org/W3127085283
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:234021801
Keywords
Computer science, Web page, Information retrieval, The Internet, Artificial intelligence
References
- A Study of Different Transfer Functions for Binary Version of Particle Swarm Optimization
- Web page classification based on a simplified swarm optimization
- An approach to feature selection for keystroke dynamics systems based on PSO and feature weighting
- Improving Arabic Text Categorization Using Neural Network with SVD
- APT: Arabic Part-of-speech Tagger
- Grammar of the arabic language
- Particle swarm optimisation with spatial particle extension
- Arabic text classification using Polynomial Networks
- Vector Space Models to Classify Arabic Text
- Term-Weighting Approaches in Automatic Text Retrieval
- Feature sub-set selection metrics for Arabic text classification
- A Simple Study of Webpage Text Classification Algorithms for Arabic and English Languages
- Application of Taguchi method in the optimization of end milling parameters
- S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization
- Improving Arabic document categorization: Introducing local stem
- Stemming Versus Light Stemming as Feature Selection Techniques for Arabic Text Categorization
- The hybrid feature selection k-means method for Arabic webpage classification
- Parameter tuning of particle swarm optimization by using Taguchi method and its application to motor design
- Improving stemming for Arabic information retrieval: light stemming and co-occurrence analysis
- Comparative evaluation of text classification techniques using a large diverse Arabic dataset
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