From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Data mining based on rough sets
Data mining
Quality Measures in Data Mining (Studies in Computational Intelligence)
Quality Measures in Data Mining (Studies in Computational Intelligence)
Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support
Engineering Applications of Artificial Intelligence
Bioinformatics
Evaluating Importance of Conditions in the Set of Discovered Rules
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
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
AIMSA'12 Proceedings of the 15th international conference on Artificial Intelligence: methodology, systems, and applications
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
Hi-index | 0.00 |
In this paper we present a method for evaluating the importance of GO terms which compose multi-attribute rules. The rules are generated for the purpose of biological interpretation of gene groups. Each multi-attribute rule is a combination of GO terms and, based on relationships among them, one can obtain a functional description of gene groups. We present a method which allows evaluating the influence of a given GO term on the quality of a rule and the quality of a whole set of rules. For each GO term, we compute how big its influence on the quality of generated set of rules and therefore the quality of the obtained description is. Based on the computed quality of GO terms, we propose a new algorithm of rule induction in order to obtain a more synthetic and more accurate description of gene groups than the description obtained by initially determined rules. The obtained GO terms ranking and newly obtained rules provide additional information about the biological function of genes that compose the analyzed group of genes.