Performance Evaluation of Classification Methods in Layout Prediction of Web Pages

• Published in: 2018 International Conference on Artificial Intelligence and Data Processing (IDAP) pp. 1 - 7
• Main Authors: Ozhan, Erkan, Uzun, Erdinc
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2018
• Subjects: Classification algorithms; classification methods; Computational modeling; Decision trees; Feature extraction; imbalanced dataset; Layout; layout detection; Vegetation; web data extraction; Web pages
• DOI: 10.1109/IDAP.2018.8620893

The Web is an invaluable source of data stored on web pages. These data are contained in HTML layout elements of a web page. It is a crucial issue to extract data automatically from a web page. In this study, a dataset, which is annotated with seven different layouts including main content, headline, summary, other necessary layouts, menu, link, and other unnecessary layouts, is used. Then, 49 different features are computed from these layouts. Finally, we compare the different classification methods for evaluating the performance of these methods in layout prediction. The experiments show that the Random Forest classifier achieves a high accuracy of 98.46%. Thanks to this classifier, the prediction of link layout has a higher performance (approximately 0.988 f-Measure) according to the performance of the prediction of other layouts. On the other hand, the prediction of the summary layout has the worst performance with about 0.882 f-Measure.