International Journal of Man-Machine Studies
Comparison of the probabilistic approximate classification and the fuzzy set model
Fuzzy Sets and Systems
Rough sets: probabilistic versus deterministic approach
International Journal of Man-Machine Studies
A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
Variable precision rough set model
Journal of Computer and System Sciences
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Computers and Industrial Engineering
The product structure of fuzzy rough sets on a group and the rough T-fuzzy group
Information Sciences: an International Journal
Development of a measure model for optimal planning of maintenance and improvement of roads
Computers and Industrial Engineering
International Journal of Approximate Reasoning
Fuzzy reasoning based on a new fuzzy rough set and its application to scheduling problems
Computers & Mathematics with Applications
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
The evaluation of consumer loans using support vector machines
Expert Systems with Applications: An International Journal
Guest editorial: Advances in fuzzy sets and rough sets
International Journal of Approximate Reasoning
Rough membership and bayesian confirmation measures for parameterized rough sets
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
For decision tables with a very small number of objects, this paper introduces a model combining the equivalence relations of rough set theory and algebraic structures. As an example application, we classify six-dimensional attribute vectors (maintenance quality indices) in a sample of 55 bridges from Chongqing province in China. A panel of experts has already used these data to rank the sites in terms of overall management quality, and full use is made of their decisions in training the model. Compared with the performances of a classical rough set model and a support vector machine, the new model is shown to be both feasible and more accurate.