Full Content-based Web Page Classification Methods by using Deep Neural Networks
| Autor: | Fargana J. Abdullayeva, Suleyman Suleymanzade |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Control and Optimization Information retrieval Computer science business.industry Deep learning Data classification computer.software_genre News aggregator Data aggregator ComputingMethodologies_PATTERNRECOGNITION Artificial Intelligence Signal Processing Web page Computer Vision and Pattern Recognition Artificial intelligence Statistics Probability and Uncertainty Web crawler business Representation (mathematics) computer Classifier (UML) Information Systems |
| Zdroj: | Statistics, Optimization & Information Computing. 9:963-973 |
| ISSN: | 2310-5070 2311-004X |
| DOI: | 10.19139/soic-2310-5070-1056 |
| Popis: | The quality of the web page classification process has a huge impact on information retrieval systems. In this paper, we proposed to combine the results of text and image data classifiers to get an accurate representation of the web pages. To get and analyse the data we created the complicated classifier system with data miner, text classifier, and aggregator. The process of image and text data classification has been achieved by the deep learning models. In order to represent the common view onto the web pages, we proposed three aggregation techniques that combine the data from the classifiers. |
| Databáze: | OpenAIRE |
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