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High-Quality Train Data Generation for Deep Learning-Based Web Page Classification Models by Kim, Jeong-Jae (author);On, Byung-Won (author);Lee, Ingyu (author) is a Engineering article available to read on EtoBox.

What is High-Quality Train Data Generation for Deep Learning-Based Web Page Classification Models about?

The current deep learning models detecting relevant web pages show low accuracy because of the poor quality of the training data. In this paper, we propose a novel algorithm to automatically generate high-quality training data based on the frequency of the document including the entity of interest. Our experimental results with movies and cellphones data sets show that the average F 1 -score of the deep learning models (FNN, CNN, Bi-LSTM, and SeqGAN) trained with our proposed algorithm shows up to 0.9992 in F 1 -score.

Who reads High-Quality Train Data Generation for Deep Learning-Based Web Page Classification Models?

Author

Kim, Jeong-Jae (author);On, Byung-Won (author);Lee, Ingyu (author)

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Engineering (Physical Sciences)