Affiliation:
1. IT Department, College of Applied Sciences, IBRI, BOX 516, Sultanate of Oman
Abstract
This paper proposes an adaptive centroid-based classifier (ACC) for multi-label classification of web pages. Using a set of multi-genre training dataset, ACC constructs a centroid for each genre. To deal with the rapid evolution of web genres, ACC implements an adaptive classification method where web pages are classified one by one. For each web page, ACC calculated its similarity with all genre centroids. Based on this similarity, ACC either adjusts the genre centroid by including the new web page or discards it. A web page is a complex object that contains different sections belonging to different genres. To handle this complexity, ACC implements a multi-label classification where a web page can be assigned to multiple genres at the same time. To improve the performance of genre classification, we propose to aggregate the classifications produced using character n-grams extracted from URL, title, headings and anchors. Experiments conducted using a known multi-label dataset show that ACC outperforms many other multi-label classifiers and has the lowest computational complexity.
Publisher
World Scientific Pub Co Pte Lt
Subject
Library and Information Sciences,Computer Networks and Communications,Computer Science Applications
Cited by
5 articles.
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