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On the use of ensemble of classifiers for accelerometer-based activity recognition

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On the use of ensemble of classifiers for accelerometer-based activity recognition _.pdf (336.9Kb)
Author
Çatal, Çağatay
Tüfekçi, Selin
Pirmit, Elif
Kocabağ, Güner
Type
Article
Date
2015-12
Language
en_US
Metadata
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Abstract
Activity recognition aims to detect the physical activities such as walking, sitting, and jogging performed by humans. With the widespread adoption and usage of mobile devices in daily life, several advanced applications of activity recognition were implemented and distributed all over the world. In this study, we explored the power of ensemble of classifiers approach for accelerometer-based activity recognition and built a novel activity prediction model based on machine learning classifiers. Our approach utilizes from J48 decision tree, Multi-Layer Perceptrons (MLP) and Logistic Regression techniques and combines these classifiers with the average of probabilities combination rule. Publicly available activity recognition dataset known as WISDM (Wireless Sensor Data Mining) which includes information from thirty six users was used during the experiments. According to the experimental results, our model provides better performance than MLP-based recognition approach suggested in previous study. These results strongly suggest researchers applying ensemble of classifiers approach for activity recognition problem. (C) 2015 Elsevier B.V. All rights reserved.
Subject
Activity recognition
Sensor mining
Mobile computing
Accelerometer
Ensemble of classifiers
Machine learning
URI
https://doi.org/10.1016/j.asoc.2015.01.025
https://hdl.handle.net/11413/2017
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  • Makaleler / Articles [100]
  • Scopus Publications [724]
  • WoS Publications [1016]

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İstanbul Kültür University

Hakkında |Politika | Kütüphane | İletişim | Send Feedback | Admin

Istanbul Kültür University, Ataköy Campus E5 Karayolu Üzeri Bakırköy 34158, İstanbul / TURKEY
Copyright © İstanbul Kültür University

Creative Commons Lisansı
IKU Institutional Repository, Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.

Designed by  UNIREPOS