کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6902214 | 1446500 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Experimental Study Of Feature Weighting Techniques For URL Based Webpage Classification
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
Information retrieval task has become a difficult task due to the growing size of the web. This demands a simple method for classifying the web pages. We propose an URL based approach, as it avoids downloading the web page contents. Feature weighing methods play an important role in improving the performance of a classifier. In this paper, we explored different weighting methods and conducted various experiments on WebKB dataset. Results show that tf.mi feature weighting technique achieves F1 measure of 79% and outperforms other weighting methods, which is an improvement of 19.6% over existing works on URL based classification.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Procedia Computer Science - Volume 115, 2017, Pages 218-225
Journal: Procedia Computer Science - Volume 115, 2017, Pages 218-225
نویسندگان
R. Rajalakshmi, Sanju Xaviar,