Clonal Selection based AIS Weighted Feature Extraction Algorithm to identify the Multiclass web Pages Categories
Karunendra Verma1, Prateek Srivastava2, Amit Jain3
1Dr. Amit Jain, Assistant Professor, Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur (Rajasthan), India.
2Prateek Srivastava, M. Tech Degree, Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur (Rajasthan), India.
3Karunendra Verma Department of CSE, Sir Padampat Singhania University, Udaipur (Rajasthan), India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 20 March 2019 | Manuscript published on 30 March 2019 | PP: 156-161 | Volume-8 Issue-5, March 2019 | Retrieval Number: E2927238519/19©BEIESP
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Due to the unbelievable increment in the assess of data on the World Wide Web, there is a solid requirement to optimize a web page cataloging to reclaim constructive information rapidly. Proposed clonal selection based artificial immune system algorithm to select the most excellent weights for every feature in the training dataset and implement the KNN (k-Nearest Neighbour) classifier to categorized the new web pages from testing dataset. In addition, the weight determination process is depended on both term and tag weighting method. Structure features are gathered and appointed weights in this scheme. Results obtained show that projected classifier effectively classified to demonstrate the efficacy of the algorithm with respect to single and multi-class.
Keyword: Artificial Immune System, K-Nearest Neighbour, Tag Weighting, Term Weighting, Web Page Classification.
Scope of the Article: Classification.