Web Link Spam Identification Inspired by Artificial Immune System and the Impact of TPP-FCA Feature Selection on Spam Classification

Search engines are the doorsteps for retrieving required information from the web. Web spam is a bad method for improving the ranking and visibility of the web pages in search engine results. This paper addresses the problem of the link spam classification through the features of the web sites. Link related features retrieved from the website are used to discriminate the spam and non-spam sites. AIS inspired algorithms are applied for the dataset and results are evaluated. Artificial immune systems are machine learning systems inspired by the principles of the natural immunology. It comprises of supervised learning schemes which can be adapted for the wide range of the classification problems.UK- WEBSPAM-2007 Dataset [8] is used for the experiments. WEKA [9] is used to simulate the classifiers. Artificial Immune Recognition algorithm seems to perform well than the other classes. Best classification accuracy attained is 98.89 by AIRS1 Algorithm. This seems to be good when comparing with the other classifiers accuracy available on the existing literature.