Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Rule Evaluation Measures: A Unifying View
ILP '99 Proceedings of the 9th International Workshop on Inductive Logic Programming
Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support
Engineering Applications of Artificial Intelligence
Comparing Accuracies of Rule Evaluation Models to Determine Human Criteria on Evaluated Rule Sets
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
International Journal of Applied Mathematics and Computer Science
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Methods for automatic functional description of gene groups are useful tools supporting the interpretation of biological experiments. The RuleGO algorithm provides functional interpretation of gene groups in a form of logical rules including combinations of Gene Ontology terms in their premises. The number of rules generated by the algorithm is usually huge and additional methods of rule quality evaluation and filtration are required in order to select the most interesting ones. In the paper, we apply the multicriteria decision making UTA method to obtain a ranking of rules based on subjective expert opinion which is provided in a form of an ordered list of several rules. The presented approach is applied to the well known data set from microarray experiment and the results are compared with the standard RuleGO compound rule quality measure.