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

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.