INFER: an adaptative decision support system based on the probabilistic approximate classification
6th Internation Workshop Vol. 1 on Expert Systems & Their Applications
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
Relational interpretations of neighborhood operators and rough set approximation operators
Information Sciences—Informatics and Computer Science: An International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
On the Unknown Attribute Values in Learning from Examples
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Probabilistic rough set approximations
International Journal of Approximate Reasoning
Generalized parameterized approximations
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Definability and other properties of approximations for generalized indiscernibility relations
Transactions on Rough Sets XI
Experiments on rule induction from incomplete data using three probabilistic approximations
GRC '12 Proceedings of the 2012 IEEE International Conference on Granular Computing (GrC-2012)
Hi-index | 0.00 |
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.