A Modified Boosted Ensemble Classifier on Location Based Social Networking

Abstract

One of the research issues that researchers are interested in is unbalanced dataclassification techniques. Boosting approaches like Wang's Boosting and ModifiedBoosted SVM (MBSVM) have been demonstrated to be more effective for unbalanceddata. Our proposal The Modified Boosted Random Forest (MBRF) classifier is aRandom Forest classifier that uses the Boosting approach. The main motivation of thestudy is to analyze sentiment of geotagged tweets understanding the state of mind ofpeople at FIFA and Olympics datasets. Tree based model Random Forest algorithmusing boosting approach classifies the tweets to build a recommendation system withan idea of providing commercial suggestions to participants, recommending localplaces to visit or perform activities. MBRF employs various strategies: i) a distancebasedweight-update method based on K-Medoids ii) a sign-based classifier eliminationtechnique. We have equally partitioned the datasets as 70% of data allocated fortraining and the remaining 30% data as test data. Our imbalanced data ratio measured3.1666 and 4.6 for FIFA and Olympics datasets. We looked at accuracy, precision,recall and ROC curves for each event. The average AUC achieved by MBRF on FIFAdataset is 0.96 and Olympics is 0.97. A comparison of MBRF and Decision tree modelusing 'Entropy' proved MBRF better.