Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data

Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data

Daily living activities (DLAs) classification using data collected from wearable monitoring sensors is very challenging due to the imbalance characteristics of the monitored data. A major research challenge is to determine the best combination of features that returns the best accuracy results using...

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Bibliographic Details
Main Authors: Dorin Moldovan, Ionut Anghel, Tudor Cioara, Ioan Salomie
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Applied Sciences
Subjects:
machine learning
data streams
daily living activities
classification
wearable devices
Online Access:https://www.mdpi.com/2076-3417/10/4/1496

Internet

https://www.mdpi.com/2076-3417/10/4/1496

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