This article is cited in 9 papers

NUMERICAL METHODS AND DATA ANALYSIS

Anomaly detection in an ecological feature space to improve the accuracy of human activity identification in buildings

I. M. Kulikovskikh

Samara National Research University, Samara, Russia

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.

Keywords: anomaly detection, logistic regression, machine learning, Cox-Box transformation, detection system, ecological feature.

Received: 05.12.2016
Accepted: 07.01.2017

DOI: 10.18287/2412-6179-2017-41-1-126-133