Website accessibility sampling evaluation based on KNN and local regression
CHEN Rong-hua1, WANG Ying-han2, BU Jia-jun3, YU Zhi3, GAO Fei4
1. College of Information Engineering, Jiangxi Vocational College of Finance & Economics, Jiujiang 332000, China;
2. Department of Information Engineering Shangrao Vocational & Technical College, Shangrao 334109, China;
3. Zhejiang Provincial Key Laboratory of Service Robot, College of Computer Science, Zhejiang University, Hangzhou 310027, China;
4. College of Information Engineering, Putian University, Putian 351100, China
A novel sampling evaluation algorithm was proposed for a given page based on the KNN evaluated samples. As sampling in a large website tends to be sparse, KNN may lead to a high evaluation bias and a local regression model was thus employed to improve the quality of KNN-based evaluation. First, a certain number of webpages were randomly selected from a website and evaluated to obtain an initial website accessibility score. Each evaluated webpage was treated as a labeled sample and the accessibility scores for the rest pages in the website were estimated using local regression on the KNN. The experimental results validate that the proposed algorithm has significant improvement over the random sampling algorithm in website accessibility evaluation.
Received: 08 November 2017
Published: 20 September 2018
CHEN Rong-hua, WANG Ying-han, BU Jia-jun, YU Zhi, GAO Fei. Website accessibility sampling evaluation based on KNN and local regression. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2018, 52(9): 1702-1708.
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