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Analysis of Self-Injurious Behavior by the LERS Data Mining System

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New Frontiers in Artificial Intelligence (JSAI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2253))

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Abstract

A large number of individuals with disabilities engage in problem behaviors which are influenced by environmental and social factors [16]. A smaller, but significant, proportion of problem behaviors appear to maintained by physiological events [4], [11], [16]. Over time, problem behavior maintained primarily by physiological events may be influenced by environmental factors [3]. For these reasons, a more sophisticated assessment approach that considers the interrelation between physiological and environmental factors is needed [9], [16].

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Freeman, R.L., Grzymala-Busse, J.W., Riffel, L.A., Schroeder, S.R. (2001). Analysis of Self-Injurious Behavior by the LERS Data Mining System. In: Terano, T., Ohsawa, Y., Nishida, T., Namatame, A., Tsumoto, S., Washio, T. (eds) New Frontiers in Artificial Intelligence. JSAI 2001. Lecture Notes in Computer Science(), vol 2253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45548-5_53

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  • DOI: https://doi.org/10.1007/3-540-45548-5_53

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  • Print ISBN: 978-3-540-43070-4

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