Abstract
Intensive development of artificial intelligence methods in recent years has led to a great interest in this subject among scientists and psychologists. This interest has led to the increasing use of these methods in the humanities and social sciences. Since this is a relatively new area of applications of artificial intelligence, most of research concentrates on the improvement of the use of these new tools in psychology. The present work focuses on the comparison of the precision and effectiveness of several known algorithms for, among others, recognizing patterns in the classification of human traits and behaviors, so that in the future one can automate this process and find reliable methods to predict these behaviors. Specifically, some three most interesting algorithms were selected and their effectiveness in the pattern recognition tested on selected data.
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References
Chetouani M, Cohn J, Salah AA (eds) (2016) Human behavior understanding. In: LNCS 9997, proceedings of the 7th international workshop, Amsterdam, The Netherlands, Oct 16
Emirsajłow M (2019) Pattern recognition methods in human behaviour analysis. MSc thesis, faculty of electronics, Wroclaw University of Science and Technology, Wrocław
John OP, Srivastava S (1999) The big five trait taxonomy: history, measurement, and theoretical perspectives. Handb Pers: Theory Res 2:102–138
Kroenke K, Spitzer RL, Williams JB (2001) The PHQ-9. J Gen Intern Med 16(9):606–613
Pentland A, Liu A (1999) Modeling and prediction of human behavior. Neural Comput 11(1):229–242
Statistics and Machine Learning Toolbo\(^{{{\rm TM}}}\) User’s Guide, Matlab R2019a. The MathWorks (2019)
Russell DW (1996) UCLA loneliness scale (version 3): reliability, validity, and factor structure. J Pers Assess 66(1):20–40
Salah AA, Gevers T (eds) (2011) Computer analysis of human behavior. Springer, London
Shalev-Shwartz Shai, Ben-David Shai (2014) Understanding machine learning. From theory to algorithms. Cambridge University Press, New York
Salah AA, Krose BJA, Cook DJ (eds) Human Behavior Understanding. In: LNCS 9277, proceedings of the 6th international workshop, Osaka, Japan, Sept 8
Wang R, Chen F, Chen Z, Li T, Harari G, Tignor S, Zhou X, Ben-Zeev D, Campbell A, Student life: assessing mental health, academic performance and behavioral trends of college students using smartfons. In: Proceedings of ACM conference on ubiquitous computing (2014)
Project StudentLife (2019). http://studentlife.cs.dartmouth.edu/. Accessed 14 June 2019
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Emirsajłow, M., Jeleń, Ł. (2022). Analysis of Effectiveness of Selected Classifiers for Recognizing Psychological Patterns. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore. https://doi.org/10.1007/978-981-16-2380-6_45
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DOI: https://doi.org/10.1007/978-981-16-2380-6_45
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