Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support
Engineering Applications of Artificial Intelligence
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Decision rule-based data models using TRS and NetTRS – methods and algorithms
Transactions on Rough Sets XI
Rule quality measures in creation and reduction of data rule models
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Supervised learning in the gene ontology part i: a rough set framework
Transactions on Rough Sets IV
Supervised learning in the gene ontology part II: a bottom-up algorithm
Transactions on Rough Sets IV
Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Evaluation of semantic term and gene similarity measures
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
CHIRA---Convex Hull Based Iterative Algorithm of Rules Aggregation
Fundamenta Informaticae
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
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A rules induction algorithm dedicated to describe groups of genes with similar expression profiles by means of Gene Ontology terms is discussed in the paper. The presented algorithm takes into consideration information contained in the Gene Ontology graph. A huge number of created rules requires defining the rules quality and similarity measures, thus the paper presents such measures and proposes a new method of the most interesting rules selection. Features reduction method based on the rough sets theory is adopted and applied in order to reduce the number of Gene Ontology terms occurring in rules. The paper presents results of performed experiments and describes shortly the internet application RuleGO in which the proposed methods were implemented.