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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 1/2018

16.08.2016 | Theoretical Advances

The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition

verfasst von: Bilal M’hamed Abidine, Lamya Fergani, Belkacem Fergani, Mourad Oussalah

Erschienen in: Pattern Analysis and Applications | Ausgabe 1/2018

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Abstract

Two serious problems affecting the implementation of human activity recognition algorithms have been acknowledged. The first one corresponds to non-informative sequence features. The second is the class imbalance in the training data due to the fact that people do not spend the same amount of time on the different activities. To address these issues, we propose a new scheme based on a combination of principal component analysis, linear discriminant analysis (LDA) and the modified weighted support vector machines. First we added the most significant principal components to the set of features extracted using LDA. This work shows that a suitable sequence feature set combined with the modified WSVM based on our criterion classifier achieves good improvement and efficiency over the traditional used methods.
Vorheriger Artikel Task sensitivity in EEG biometric recognition
Nächster Artikel VPVC: verifiable progressive visual cryptography

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Metadaten
Titel
The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition
verfasst von
Bilal M’hamed Abidine
Lamya Fergani
Belkacem Fergani
Mourad Oussalah
Publikationsdatum
16.08.2016
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 1/2018
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-016-0570-y

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