TY  - CONF
ID  - moewes_evolutionary_2011
T1  - Evolutionary Fuzzy Rules for Ordinal Binary Classification with Monotonicity Constraints
A1  - Moewes, Christian
A1  - Kruse, Rudolf
ED  - Yager, Ronald R.
ED  - Abbasov, Ali M.
ED  - Reformat, Marek Z.
ED  - Shahbazova, Shahnaz N.
TI  - Soft Computing: State of the Art Theory and Novel Applications
T3  - Studies in Fuzziness and Soft Computing
Y1  - 2013
VL  - 291
IS  - 2941
SP  - 105
EP  - 112
PB  - Springer
T2  - San Francisco State University
CY  - San Francisco, CA, USA
AD  - Berlin Heidelberg
SN  - 978-3-642-34921-8
N1  - Proceedings of the World Conference on Soft Computing, May 23--26, 2011
UR  - http://link.springer.com/chapter/10.1007/978-3-642-34922-5_8
M2  - doi: 10.1007/978-3-642-34922-5_8
KW  - Binary Classification
KW  - Evolutionary Algorithms
KW  - Fuzzy Rules
KW  - Monotonicity Constraints
N2  - We present an approach to learn fuzzy binary decision rules from ordinal temporal data where the task is to classify every instance at each point in time. We assume that one class is preferred to the other, e.g. the undesirable class must not be misclassified. Hence it is appealing to use the Variable Consistency Dominance-based Rough Set Approach (VC-DRSA) to exploit preference information about the problem. In this framework, the VC-DomLEM algorithm has been used to generate the minimal set of consistent rules. Every attribute is then fuzzified by first applying a crisp clustering to the rules’ antecedent thresholds and second using the cluster centroids as indicator for the overlap of neighboring trapezoidal normal membership functions. The widths of the neighboring fuzzy sets are finally tuned by an evolutionary algorithm trying to minimize the specificity of the current fuzzy rule base.
ER  -