Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data preparation for data mining
Data preparation for data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Representative Association Rules and Minimum Condition Maximum Consequence Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Data Mining In Bioinformatics (Advanced Information and Knowledge Processing)
Data Mining In Bioinformatics (Advanced Information and Knowledge Processing)
Bioinformatics Technologies
New Evolutionary Bankruptcy Forecasting Model Based on Genetic Algorithms and Neural Networks
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
IGB: a new informative generic base of association rules
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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To avoid obtaining an unmanageable highly sized association rule sets– compounded with their low precision– that often make the perusal of knowledge ineffective, the extraction and exploitation of compact and informative generic basis of association rules is a becoming a must. Moreover, they provide a powerful verification technique for hampering gene mis-annotating or badly clustering in the Unigene library. However, extracted generic basis is still oversized and their exploitation is impractical. Thus, providing critical nuggets of extra-valued knowledge is a compellingly addressable issue. To tackle such a drawback, we propose in this paper a novel approach, called EGEA (Evolutionary Gene Extraction Approach). Such approach aims to considerably reduce the quantity of knowledge, extracted from a gene expression dataset, presented to an expert. Thus, we use a genetic algorithm to select the more predictive set of genes related to patient situations. Once, the relevant attributes (genes) have been selected, they serve as an input for a second approach stage, i.e., extracting generic association rules from this reduced set of genes. The notably decrease of the generic association rule cardinality, extracted from the selected gene set, permits to improve the quality of knowledge exploitation. Carried out experiments on a benchmark dataset pointed out that among this set, there are genes which are previously unknown prognosis-associated genes. This may serve as molecular targets for new therapeutic strategies to repress the relapse of pediatric acute myeloid leukemia (AML).