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

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

Ahsan Hussain, Bettahally N. Keshavamurthy, Seema Wazarkar
Copyright: © 2019 |Volume: 16 |Issue: 2 |Pages: 18
ISSN: 1545-7362|EISSN: 1546-5004|EISBN13: 9781522563990|DOI: 10.4018/IJWSR.2019040103
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MLA

Hussain, Ahsan, et al. "A Novel Multi-Layer Classification Ensemble Approach for Location Prediction of Social Users." IJWSR vol.16, no.2 2019: pp.47-64. http://doi.org/10.4018/IJWSR.2019040103

APA

Hussain, A., Keshavamurthy, B. N., & Wazarkar, S. (2019). A Novel Multi-Layer Classification Ensemble Approach for Location Prediction of Social Users. International Journal of Web Services Research (IJWSR), 16(2), 47-64. http://doi.org/10.4018/IJWSR.2019040103

Chicago

Hussain, Ahsan, Bettahally N. Keshavamurthy, and Seema Wazarkar. "A Novel Multi-Layer Classification Ensemble Approach for Location Prediction of Social Users," International Journal of Web Services Research (IJWSR) 16, no.2: 47-64. http://doi.org/10.4018/IJWSR.2019040103

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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.

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