Abstract:
This paper considers a problem of improving the accuracy of identifying human activity in buildings based on an ecological feature space. To solve this problem a model of logistic regression was implemented on the assumption of the unstable estimation of logistic regression parameters
for near linearly separable classes. To reach a compromise between the presence of outliers and the accuracy of recognition an algorithm of anomaly detection was proposed. Computational experiments confirmed the effectiveness of the algorithm and its theoretical consistency.
This work was supported by the Ministry of Education and Science of the Russian Federation, grant 074-U01.
Received: 05.12.2016 Accepted: 07.01.2017
Document Type:
Article
Language: Russian
Citation:
I. M. Kulikovskikh, “Anomaly detection in an ecological feature space to improve the accuracy of human activity identification in buildings”, Computer Optics, 41:1 (2017), 126–133
\Bibitem{Kul17}
\by I.~M.~Kulikovskikh
\paper Anomaly detection in an ecological feature space to improve the accuracy of human activity identification in buildings
\jour Computer Optics
\yr 2017
\vol 41
\issue 1
\pages 126--133
\mathnet{http://mi.mathnet.ru/co366}
\crossref{https://doi.org/10.18287/2412-6179-2017-41-1-126-133}
Linking options:
https://www.mathnet.ru/eng/co366
https://www.mathnet.ru/eng/co/v41/i1/p126
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