A Novel Multi-Layer Classification Ensemble Approach for Location Prediction of Social Users

Ahsan Hussain, Bettahally N. Keshavamurthy and Seema Wazarkar
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Ahsan Hussain: National Institute of Technology Goa, Ponda, India
Bettahally N. Keshavamurthy: National Institute of Technology Goa, Ponda, India
Seema Wazarkar: National Institute of Technology Goa, Ponda, India

International Journal of Web Services Research (IJWSR), 2019, vol. 16, issue 2, 47-64

Abstract: Information-disclosure by social-users has increased enormously. Using this information for accurate location-prediction is challenging. Thus, a novel Multi-Layer Ensemble Classification scheme is proposed. It works on un-weighted/weighted majority voting, using novel weight-assignment function. Base learners are selected based on their individual performances for training the model. Main motive is to develop an efficient approach for check-ins-based location-classification of social-users. The proposed model is implemented on Foursquare datasets where a classification accuracy of 94% is achieved, which is higher than other state-of-the-art techniques. Apart from tracking locations of social-users, proposed framework can be useful for detecting malicious users present in various expert and intelligent-system.

Date: 2019
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