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Dynamic Multi-layer Ensemble Classification Framework for Social Venues Using Binary Particle Swarm Optimization

  • Ahsan HussainEmail author
  • Bettahally N. Keshavamurthy
  • Ramalingaswamy Cheruku
Article

Abstract

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.

Keywords

Location-Based Social Networks Social-venue classification Machine learning Majority voting Dynamic multi-layer ensembles User-checkins 

Notes

Acknowledgements

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology GoaPondaIndia

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