Quantitative evaluation of web metrics for automatic genre classification of web pages

Ruchika Malhotra () and Anjali Sharma ()
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Ruchika Malhotra: Delhi Technological University
Anjali Sharma: Dr K S Krishnan Marg

International Journal of System Assurance Engineering and Management, 2017, vol. 8, issue 2, No 80, 1567-1579

Abstract: Abstract An additional dimension that facilitate a swift and relevant response from a web search engine is to introduce a genre class for each web page. The web genre classification distinguishes between pages by means of their features such as functionality, style, presentation layout, form and meta-content rather than on content. In this work, nineteen web metrics are identified according to the lexical, structural and functionality attributes of the web page rather than topic. The study is carried out to determine which of these attributes (lexical, structural and functionality) or its combinations, are significant for the development of web genre classification model. Also, we investigate the best web genre prediction model using parametric (Logistic Regression), non-parametric (Decision Tree) and ensemble (Bagging, Boosting) machine learning algorithms. We built forty-two genre classification models to classify web pages into Movie, TV or Music genre using a sample space data extracted from the Pixel Awards nominated and award winning websites. Our results obtained from the area under the curve analysis of these forty-two models show that the ensemble algorithms provide better performance. The rest of the models have acceptable performance, only in cases for which the lexical and structural attributes were fed in combination. Functionality metrics were found to considerably degrade the performance measure, irrespective of the algorithm used. The overall results of the study indicate the predictive capability of machine learning models for web genre classification, provided an appropriate choice is made on the selection of the input metrics.

Keywords: Web metrics; Web genre classification; Machine learning; Entertainment websites (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s13198-017-0629-1

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