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    <p>I think that <a href="https://stackoverflow.com/questions/2425549/a-good-machine-learning-technique-to-weed-out-good-urls-from-bad/2425625#2425625">steve</a> and <a href="https://stackoverflow.com/questions/2425549/a-good-machine-learning-technique-to-weed-out-good-urls-from-bad/2432344#2432344">StompChicken</a> both make excellent points:</p> <ul> <li><strong>Picking the best algorithm is tricky</strong>, even for machine learning experts. Using <a href="http://www.cs.waikato.ac.nz/ml/weka/" rel="nofollow noreferrer">a general-purpose package like Weka</a> will let you easily compare a bunch of different approaches to determine which works best for your data.</li> <li><strong>Choosing good features</strong> is often one of the most important factors in how well a learning algorithm will work.</li> </ul> <p>It could also be useful to examine how other people have approached similar problems:</p> <ul> <li>Qi, X. and Davison, B. D. 2009. <a href="http://www.cse.lehigh.edu/~brian/pubs/2007/classification-survey/LU-CSE-07-010.pdf" rel="nofollow noreferrer">Web page classification: Features and algorithms</a>. ACM Computing Survey 41, 2 (Feb. 2009), 1-31.</li> <li>Kan, M.Y. and H.O.N. Thi (2005). <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.89.7701&amp;rep=rep1&amp;type=pdf" rel="nofollow noreferrer">Fast webpage classification using URL features</a>. In <em>Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM ’05)</em>, New York, NY, pp. 325–326.</li> <li>Devi, M. I., Rajaram, R., and Selvakuberan, K. 2007. <strong>Machine Learning Techniques for Automated Web Page Classification Using URL Features</strong>. In <em>Proceedings of the international Conference on Computational intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02</em> (December 13 - 15, 2007). Washington, DC, pp. 116-120.</li> </ul>
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    1. CO+1 For links to relevant articles that don't require a subscription to view.
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