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

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.