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An Experimental Comparison of Three Probabilistic Approximations Used for Rule Induction

Clark P. G., Grzymala-Busse J.W.

Fundamenta Informaticae

2013

Vol. 127, nr 1-4

177--191

In this paper we present results of experiments on 166 incomplete data sets using three probabilistic approximations: lower, middle, and upper. Two interpretations of missing attribute values were used: lost and “do not care” conditions. Our main objective was to select the best combination of an approximation and a missing attribute interpretation. We conclude that the best approach depends on the data set. The additional objective of our research was to study the average number of distinct probabilities associated with characteristic sets for all concepts of the data set. This number is much larger for data sets with “do not care” conditions than with data sets with lost values. Therefore, for data sets with “do not care” conditions the number of probabilistic approximations is also larger.

Non-singleton fuzzy models

Rutkowska D.

Applied Mathematics and Computer Science

1998

Vol. 8, no 2

435-444

This paper presents an approach to design non-singleton fuzzy-logic systems. The relation between non-singleton and singleton fuzzy-logic systems is derived. Simulation results illustrate the effect of uncertainty of the input variables on the truck trajectories in the truck backer-upper control problem.