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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
First Online:

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 
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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.

References

  1. 1.
    Cao, X., Cong, G., & Jensen, C. S. (2010). Mining significant semantic locations from gps data. Proceedings of the VLDB Endowment, 3(1–2), 1009–1020.CrossRefGoogle Scholar
  2. 2.
    Foursquare Labs: Foursquare checkins. (2017). http://www.swarmapp.com.
  3. 3.
    Yang, J., & Olafsson, S. (2006). Optimization-based feature selection with adaptive instance sampling. Computers & Operations Research, 33(11), 3088–3106.CrossRefzbMATHGoogle Scholar
  4. 4.
    Valentini, G. & Masulli, F. (2002). Ensembles of learning machines. In Italian workshop on neural nets, (pp. 3–20). Springer.Google Scholar
  5. 5.
    Zolfaghar, K., Verbiest, N., Agarwal, J., Meadem, N., Chin, S.-C., Roy, S. B., Teredesai, A. et al. (2013). Predicting risk-of-readmission for congestive heart failure patients: A multi-layer approach. arXiv preprint arXiv:1306.2094.
  6. 6.
    Virrantaus, K., Markkula, J., Garmash, A., Terziyan, V., Veijalainen, J., Katanosov, A., & Tirri, H. (2001). Developing GIS-supported location-based services. In Proceedings of the 2nd international conference on web information systems engineering, 2001 (Vol. 2, pp. 66–75). IEEE.Google Scholar
  7. 7.
    Emersion, B. J. (2011). Using location-based services to get customers. Franchising World, 43(7), 9.Google Scholar
  8. 8.
    Shugan, S. M. (2004). The impact of advancing technology on marketing and academic research. Informs, 1, 469–475.Google Scholar
  9. 9.
    CNET: Foursquare gives out unsolicited tips on iPhone. (2013). http://news.cnet.com/8301-10233-57606718-93/foursquare-gives-outunsolicitedtips-on-iphone.
  10. 10.
    Cramer, H., Rost, M., & Holmquist, L. E. (2011). Performing a check-in: Emerging practices, norms and ’conflicts’ in location-sharing using foursquare. In Proceedings of the 13th international conference on human computer interaction with mobile devices and services (pp. 57–66). ACM.Google Scholar
  11. 11.
    Wang, D., Pedreschi, D., Song, C., Giannotti, F., & Barabasi, A.-L. (2011). Human mobility, social ties, and link prediction. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1100–1108). ACM.Google Scholar
  12. 12.
    Chorley, M. J., Whitaker, R. M., & Allen, S. M. (2015). Personality and location-based social networks. Computers in Human Behavior, 46, 45–56.CrossRefGoogle Scholar
  13. 13.
    Lindqvist, J., Cranshaw, J., Wiese, J., Hong, J., & Zimmerman, J. (2011). I’m the mayor of my house: Examining why people use foursquare-a social-driven location sharing application. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 2409–2418). ACM.Google Scholar
  14. 14.
    Schwartz, R., & Halegoua, G. R. (2015). The spatial self: Location-based identity performance on social media. New Media & Society, 17(10), 1643–1660.CrossRefGoogle Scholar
  15. 15.
    Noulas, A., Scellato, S., Mascolo, C., & Pontil, M. (2011). An empirical study of geographic user activity patterns in foursquare. ICwSM, 11, 70–573.Google Scholar
  16. 16.
    Getoor, L., & Diehl, C. P. (2005). Link mining: A survey. ACM SIGKDD Explorations Newsletter, 7(2), 3–12.CrossRefGoogle Scholar
  17. 17.
    Goswami, A., & Kumar, A. (2017). Challenges in the analysis of online social networks: A data collection tool perspective. Wireless Personal Communications, 97(3), 4015–4061.CrossRefGoogle Scholar
  18. 18.
    Bliss, C. A., Frank, M. R., Danforth, C. M., & Dodds, P. S. (2014). An evolutionary algorithm approach to link prediction in dynamic social networks. Journal of Computational Science, 5(5), 750–764.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Torabi, N., Shakibian, H., & Charkari, N. M. (2016). An ensemble classifier for link prediction in location based social network. In Proceedings of the 24th Iranian conference on electrical engineering (ICEE) (pp. 529–532). IEEE.Google Scholar
  20. 20.
    Gu, Y., Yao, Y., Liu, W., & Song, J. (2016). We know where you are: Home location identification in location-based social networks. In Proceedings of the 25th international conference on computer communication and networks (ICCCN) (pp. 1–9). IEEE.Google Scholar
  21. 21.
    Almallah, O. F., & Albayrak, S. (2017). Predicting venues in location based social network. In Proceedings of the 7th international conference on computer science, engineering and applications (CCSEA) (pp. 11–21). CSIT.Google Scholar
  22. 22.
    Tian-ran, H., Luo, J., Kautz, H., & Sadilek, A. (2016). Home location inference from sparse and noisy data: models and applications. Frontiers of Information Technology & Electronic Engineering, 17(5), 389–402.CrossRefGoogle Scholar
  23. 23.
    Cho, E., Myers, S.A., & Leskovec, J. (2011). Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1082–1090). ACM.Google Scholar
  24. 24.
    Parvin, H., MirnabiBaboli, M., & Alinejad-Rokny, H. (2015). Proposing a classifier ensemble framework based on classifier selection and decision tree. Engineering Applications of Artificial Intelligence, 37, 34–42.CrossRefGoogle Scholar
  25. 25.
    Ghasemi, E., Kalhori, H., & Bagherpour, R. (2017). Stability assessment of hard rock pillars using two intelligent classification techniques: A comparative study. Tunnelling and Underground Space Technology, 68, 32–37.CrossRefGoogle Scholar
  26. 26.
    Kotsiantis, S. B., Zaharakis, I., & Pintelas, P. (2007). Supervised machine learning: A review of classification techniques. Emerging Artificial Intelligence Applications in Computer Engineering, 160, 3–24.Google Scholar
  27. 27.
    Yeh, I.-C., & Lien, C. (2009). The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients. Expert Systems with Applications, 36(2), 2473–2480.CrossRefGoogle Scholar
  28. 28.
    Ibarguren, I., M Pérez, J., Muguerza, J., Gurrutxaga, I., & Arbelaitz, O. (2015). Coverage-based resampling: Building robust consolidated decision trees. Knowledge-Based Systems, 79, 51–67.CrossRefGoogle Scholar
  29. 29.
    Datta, S., & Das, S. (2015). Near-Bayesian support vector machines for imbalanced data classification with equal or unequal misclassification costs. Neural Networks, 70, 39–52.CrossRefzbMATHGoogle Scholar
  30. 30.
    Li, S., Zong, C., Wang, X. (2007). Sentiment classification through combining classifiers with multiple feature sets. In International conference on natural language processing and knowledge engineering, NLP-KE 2007 (pp. 135–140). IEEE.Google Scholar
  31. 31.
    Dietterich, T. G., et al. (2000). Ensemble methods in machine learning. Multiple classifier systems, 1857, 1–15.CrossRefGoogle Scholar
  32. 32.
    Kuncheva, L. I. (2004). Combining pattern classifiers: Methods and algorithms. New York: Wiley.CrossRefzbMATHGoogle Scholar
  33. 33.
    Orrite, C., Rodríguez, M., Martínez, F., & Fairhurst, M. (2008). Classifier ensemble generation for the majority vote rule. In Iberoamerican congress on pattern recognition (pp. 340–347). Springer.Google Scholar
  34. 34.
    Wang, G., Hao, J., Ma, J., & Jiang, H. (2011). A comparative assessment of ensemble learning for credit scoring. Expert Systems with Applications, 38(1), 223–230.CrossRefGoogle Scholar
  35. 35.
    Marqués, A. I., García, V., & Sánchez, J. S. (2012). Exploring the behaviour of base classifiers in credit scoring ensembles. Expert Systems with Applications, 39(11), 10244–10250.CrossRefGoogle Scholar
  36. 36.
    Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the 6th international symposium on micro machine and human science, MHS’95 (pp. 39–43). IEEE.Google Scholar
  37. 37.
    Eberhart, R. C., & Kennedy, J. (1995). Particle swarm optimization. In Proceeding of IEEE international conference on neural network, Perth, Australia (pp. 1942–1948). IEEE.Google Scholar
  38. 38.
    Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.CrossRefGoogle Scholar
  39. 39.
    Luh, G.-C., Lin, C.-Y., & Lin, Y.-S. (2011). A binary particle swarm optimization for continuum structural topology optimization. Applied Soft Computing, 11(2), 2833–2844.CrossRefGoogle Scholar
  40. 40.
    Pal, A., & Maiti, J. (2010). Development of a hybrid methodology for dimensionality reduction in mahalanobis-taguchi system using mahalanobis distance and binary particle swarm optimization. Expert Systems with Applications, 37(2), 1286–1293.CrossRefGoogle Scholar
  41. 41.
    Zeng, X.-P., Li, Y.-M., & Qin, J. (2009). A dynamic chain-like agent genetic algorithm for global numerical optimization and feature selection. Neurocomputing, 72(4), 1214–1228.CrossRefGoogle Scholar
  42. 42.
    Tasgetiren, M. F., Suganthan, P. N., & Pan, Q.-Q. (2007). A discrete particle swarm optimization algorithm for the generalized traveling salesman problem. In Proceedings of the 9th annual conference on genetic and evolutionary computation (pp. 158–167). ACM.Google Scholar
  43. 43.
    Mirjalili, S. A., & Hashim, S. Z. M. (2012). BMOA: Binary magnetic optimization algorithm. International Journal of Machine Learning and Computing, 2(3), 204.CrossRefGoogle Scholar
  44. 44.
    Rashedi, E., Nezamabadi-Pour, H., & Saryazdi, S. (2010). BGSA: Binary gravitational search algorithm. Natural Computing, 9(3), 727–745.MathSciNetCrossRefzbMATHGoogle Scholar
  45. 45.
    Mirjalili, S., & Lewis, A. (2013). S-shaped versus V-shaped transfer functions for binary particle swarm optimization. Swarm and Evolutionary Computation, 9, 1–14.CrossRefGoogle Scholar
  46. 46.
    Yang, D., Zhang, D., & Bingqing, Q. (2016). Participatory cultural mapping based on collective behavior data in location-based social networks. ACM Transactions on Intelligent Systems and Technology (TIST), 7(3), 30.Google Scholar

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© 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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