Advances in the Dempster-Shafer theory of evidence
Theoretical foundations of order-based genetic algorithms
Fundamenta Informaticae - Special issue: to the memory of Prof. Helena Rasiowa
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Interactive Gene Clustering--A Case Study of Breast Cancer Microarray Data
Information Systems Frontiers
Rough Discretization of Gene Expression Data
ICHIT '06 Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 02
Ensembles of Classifiers Based on Approximate Reducts
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P'2000)
Relevant attribute discovery in high dimensional data: application to breast cancer gene expressions
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Leukemia prediction from gene expression data—a rough set approach
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Information Sciences: an International Journal
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
International Journal of Approximate Reasoning
Dynamic rule-based similarity model for DNA microarray data
Transactions on Rough Sets XV
Rough Set Based Reasoning About Changes
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
Review article: Computational intelligence techniques in bioinformatics
Computational Biology and Chemistry
Hi-index | 0.00 |
We extend the standard rough set-based approach to be able to deal with huge amounts of numeric attributes versus small amount of available objects. We transform the training data using a novel way of non-parametric discretization, called roughfication (in contrast to fuzzification known from fuzzy logic). Given roughfied data, we apply standard rough set attribute reduction and then classify the testing data by voting among the obtained decision rules. Roughfication enables to search for reducts and rules in the tables with the original number of attributes and far larger number of objects. It does not require expert knowledge or any kind of parameter tuning or learning. We illustrate it by the analysis of the gene expression data, where the number of genes (attributes) is enormously large with respect to the number of experiments (objects).