Dynamic Multi-layer Ensemble Classification Framework for Social Venues Using Binary Particle Swarm Optimization
Multi-layer ensemble frameworks perform much better as compared to individual classifiers. However, selection of a classifier and its placement, impacts the overall performance of ensemble framework. This problem becomes very difficult, if there are more classifiers and layers. To address these problems in this paper, we design “Binary Particle Swarm Optimization” method for selection and placement of right classifiers in multi-layer ensemble model. Proposed classifier weight-assignment method is implemented to prioritize the selected classifiers. The model is simulated for the classification of social-user check-ins in Location-Based Social Network datasets. The experimental results show that the proposed ensemble model outperforms the state-of-the-art ensemble methods in the literature. It can be used by security firms, high level decision makers and various governmental organizations for tracking malicious users.
KeywordsLocation-Based Social Networks Social-venue classification Machine learning Majority voting Dynamic multi-layer ensembles User-checkins
This research work is funded by SERB, MHRD, under Grant [EEQ/-2016/000413] for Secure and Efficient Communication inside Partitioned Social Overlay Networks project, currently going on at National Institute of Technology Goa, Ponda, India.
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