International Journal of Advanced Networking and Applications

Open Access Open Access  Restricted Access Subscription Access

An Improved Support Vector Machine Classifier Using Adaboost and Genetic Algorithmic Approach towards Web Interaction Mining


Affiliations
1 PG and Research Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore-38, India
 

Predicting the objective of internet users has divergent applications in the areas such as e-commerce, entertainment in online, and several internet-based applications. The critical part of the classifying internet queries based on obtainable features namely contextual information, keywords and their semantic relationships. This research paper presents an improved support vector machine classifier that makes use of ad boost genetic algorithmic approach towards web interaction mining. Around 31 participants are chosen and given topics to search web contents. Parameters such as precision, recall and F1 score are taken for comparing the proposed classifier with the classical support vector machine. Results proved that the proposed classifier achieves better performance than that of the conventional SVM.

Keywords

Web Interaction Mining, Algorithm, Support Vector Machine, Classifier, Ad Boost, Genetic Algorithm.
User
Notifications
Font Size

  • A.Vinupriya and S. Gomathi, "Web Page Personalization and link prediction using generalized inverted index and flame clustering," 2016 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 2016, pp. 1-8.

  • A.Yang, X. Yu and Y. Liu, "Continuous KNN Join Processing for Real-Time Recommendation," 2014 IEEE International Conference on Data Mining, Shenzhen, 2014, pp. 640-649.

  • G. Dhivya, K. Deepika, J. Kavitha and V. N. Kumari, "Enriched content mining for web applications," Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on, Coimbatore, 2015, pp. 1-5.

  • J. Jojo and N. Sugana, "User profile creation based on navigation pattern for modeling user behaviour with personalised search," Current Trends in Engineering and Technology (ICCTET), 2013 International Conference on, Coimbatore, 2013, pp. 371-374.

  • A.U. R. Khan, M. B. Khan and K. Mahmood, "Cloud service for assessment of news' Popularity in internet based on Google and Wikipedia indicators," Information Technology: Towards New Smart World (NSITNSW), 2015 5th National Symposium on, Riyadh, 2015, pp. 1-8.

  • M. A. Potey, D. A. Patel and P. K. Sinha, "A survey of query log processing techniques and evaluation of web query intent identification," Advance Computing Conference (IACC), 2013 IEEE 3rd International, Ghaziabad, 2013, pp.1330-1335.

  • M. Nayrolles and A. Hamou-Lhadj, "BUMPER: A Tool for Coping with Natural Language Searches of Millions of Bugs and Fixes," 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER), Suita, 2016, pp. 649-652.

  • Monika Dhandi, Rajesh Kumar Chakrawarti, "A Comprehensive Study of Web Usage Mining", 2016 Symposium on Colossal Data Analysis and Networking (CDAN), INDORE, India, 2016, Pages: 1 - 5.

  • T. Cheng, K. Chakrabarti, S. Chaudhuri, V. Narasayya and M. Syamala, "Data services for Etailers leveraging web search engine assets," Data Engineering (ICDE), 2013 IEEE 29th International Conference on, Brisbane, QLD, 2013, pp. 1153-1164.

  • T. F. Lin and Y. P. Chi, "Application of Webpage Optimization for Clustering System on Search Engine V Google Study," Computer, Consumer and Control (IS3C), 2014 International Symposium on, Taichung, 2014, pp. 698-701.

  • T. Srivastava, P. Desikan, V. Kumar, "Web Mining – Concepts, Applications and Research Directions", Studies in Fuzziness and Soft Computing Foundations and Advances in Data Mining, Springer Berlin Heidelberg, 2013, pp 275-307.

  • X. Ye, Z. Qi, X. Song, X. He and D. Massey, "Generalized Learning of Neural Network Based Semantic Similarity Models and Its Application in Movie Search," 2015 IEEE International Conference on Data Mining Workshop (ICDMW), Atlantic City, NJ, 2015, pp. 86-93.

  • Y. C. Fan, Y. C. Chen, K. C. Tung, K. C. Wu and A. L. P. Chen, "A framework for enabling user preference profiling through Wi-Fi logs," 2016 IEEE 32nd International Conference on Data Engineering (ICDE), Helsinki, Finland, 2016, pp. 1550-1551.

  • Y. Kiyota, Y. Nirei, K. Shinoda, S. Kurihara and H. Suwa, "Mining User Experience through Crowdsourcing: A Property Search Behavior Corpus Derived from Microblogging Timelines,"

  • IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), Singapore, 2015, pp. 1721.

  • Z. Liao, Y. Song, Y. Huang, L. w. He and Q. He, "Task Trail: An Effective Segmentation of User Search Behavior," in IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 12, pp. 3090-3102, Dec. 1 2014.

  • B. Kaviyarasu, Dr. A. V. Senthil Kumar, "WEB INTERACTION MINING USING IMPROVED EXTREME LEARNING MACHINE CLASSIFIER", International Journal for Research in Science Engineering and Technology, vol. 3, no.12, pp. 45-51, 2016.

  • B. Kaviyarasu, Dr. A. V. Senthil Kumar, "WEB INTERACTION MINING USING PENTA LAYERED ARTIFICIAL NEURAL NETWORK CLASSIFIER", International Journal of Computer Science Engineering and Technology, vol.3, no.1, pp. 64-70, 2017.


Abstract Views: 193

PDF Views: 6




  • An Improved Support Vector Machine Classifier Using Adaboost and Genetic Algorithmic Approach towards Web Interaction Mining

Abstract Views: 193  |  PDF Views: 6

Authors

B. Kaviyarasu
PG and Research Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore-38, India
A. V. Senthil Kumar
PG and Research Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore-38, India

Abstract


Predicting the objective of internet users has divergent applications in the areas such as e-commerce, entertainment in online, and several internet-based applications. The critical part of the classifying internet queries based on obtainable features namely contextual information, keywords and their semantic relationships. This research paper presents an improved support vector machine classifier that makes use of ad boost genetic algorithmic approach towards web interaction mining. Around 31 participants are chosen and given topics to search web contents. Parameters such as precision, recall and F1 score are taken for comparing the proposed classifier with the classical support vector machine. Results proved that the proposed classifier achieves better performance than that of the conventional SVM.

Keywords


Web Interaction Mining, Algorithm, Support Vector Machine, Classifier, Ad Boost, Genetic Algorithm.

References