Classification of Unseen Examples under Uncertainty

Article type: Research Article

Authors: Grzymala-Busse, Jerzy W.

Affiliations: Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA. e-mail address: Jerzy@eecs.ukans.edu

Abstract: Very frequently machine learning from real-life data is affected by uncertainty. There are three main reasons for imperfection in data: incompleteness, imprecision (also called vagueness), and errors. In this paper the main emphasis is on classification of unseen examples using a rule set induced from imperfect data. The classification strategy of the machine learning system LERS is described in detail. Results of experiments with medical data sets are also reported.

DOI: 10.3233/FI-1997-303403

Journal: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 255-267, 1997