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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 4/2019

12.02.2019

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

verfasst von: Ahsan Hussain, Bettahally N. Keshavamurthy, Ramalingaswamy Cheruku

Erschienen in: Wireless Personal Communications | Ausgabe 4/2019

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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.
Vorheriger Artikel An Energy Aware Trust Based Secure Routing Algorithm for Effective Communication in Wireless Sensor Networks
Nächster Artikel Comparative Analysis of ICI Self Cancellation Techniques for Wavelet OFDM Under Different Channels in Simulink

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Metadaten
Titel
Dynamic Multi-layer Ensemble Classification Framework for Social Venues Using Binary Particle Swarm Optimization
verfasst von
Ahsan Hussain
Bettahally N. Keshavamurthy
Ramalingaswamy Cheruku
Publikationsdatum
12.02.2019
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 4/2019
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-019-06156-w

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