Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm

Abstract

Purpose -This paper proposes a forward search algorithm for detecting and identifying natural structures arising in Human-Computer Interaction (HCI) and Human Physiological Response (HCI) data. Design/Methodology/Approach The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data was also simulated based on multivariate normal distribution method and used to compare and validate our original data. Findings -Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data. Practical implications The authors conducted some of the experiments at individual residence which may affect environmental constraints. *

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