FeSNOC: A Novel Feature Selection Algorithm Based on Niche Overlapping Coefficient

Hübener, Isabel; David, Klaus

doi:10.1109/percom.2018.8444582

Cited by 1 publication

(1 citation statement)

References 39 publications

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“…To guarantee good prototype selection, we use the niche overlapping index [7], which has demonstrated promising results in a recent feature selection study [9]. The niche overlap occurs when two organismic units use the same resources or other environmental variables.…”

Section: Dissimilarity Representation Generationmentioning

confidence: 99%

Representation learning for minority and subtle activities in a smart home environment

Sanabria

Kelsey

Dobson

et al. 2019

AIS

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Daily human activity recognition using sensor data can be a fundamental task for many real-world applications, such as home monitoring and assisted living. One of the challenges in human activity recognition is to distinguish activities that have infrequent occurrence and less distinctive patterns. We propose a dissimilarity representation-based hierarchical classifier to perform two-phase learning. In the first phase, the classifier learns general features to recognise majority classes, and the second phase is to collect minority and subtle classes to identify fine difference between them. We compare our approach with a collection of state-of-the-art classification techniques on a realworld third-party dataset that is collected in a two-user home setting. Our results demonstrate that our hierarchical classifier approach outperforms the existing techniques in distinguishing users in performing the same type of activities. The key novelty of our approach is the exploration of dissimilarity representations and hierarchical classifiers, which allows us to highlight the difference between activities with subtle difference, and thus allows the identification of well-discriminating features.

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Section: Dissimilarity Representation Generationmentioning

confidence: 99%