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Open AccessArticle

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

Computer Science Department, Technical University of Cluj-Napoca, Memorandumului 28, 400114 Cluj-Napoca, Romania
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Appl. Sci. 2020, 10(4), 1496; https://doi.org/10.3390/app10041496
Received: 15 January 2020 / Revised: 6 February 2020 / Accepted: 18 February 2020 / Published: 21 February 2020
(This article belongs to the Collection Bio-inspired Computation and Applications)
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 minimal computational resources, when the data is heterogeneous and not fitted for classical algorithms that are designed for balanced low-dimensional datasets. This research article: (1) presents a modification of the classical version of the binary particle swarm optimization (BPSO) algorithm that introduces a particular type of particles called sensor particles, (2) describes the adaptation of this algorithm for data generated by sensors that monitor DLAs to determine the best positions and features of the monitoring sensors that lead to the best classification results, and (3) evaluates and validates the proposed approach using a machine learning methodology that integrates the modified version of the algorithm. The methodology is tested and validated on the Daily Life Activities (DaLiAc) dataset. View Full-Text
Keywords: machine learning; data streams; daily living activities; classification; wearable devices machine learning; data streams; daily living activities; classification; wearable devices
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MDPI and ACS Style

Moldovan, D.; Anghel, I.; Cioara, T.; Salomie, I. Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data. Appl. Sci. 2020, 10, 1496. https://doi.org/10.3390/app10041496

AMA Style

Moldovan D, Anghel I, Cioara T, Salomie I. Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data. Applied Sciences. 2020; 10(4):1496. https://doi.org/10.3390/app10041496

Chicago/Turabian Style

Moldovan, Dorin; Anghel, Ionut; Cioara, Tudor; Salomie, Ioan. 2020. "Adapted Binary Particle Swarm Optimization for Efficient Features Selection in the Case of Imbalanced Sensor Data" Appl. Sci. 10, no. 4: 1496. https://doi.org/10.3390/app10041496

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