Machine Learning - Special issue on learning with probabilistic representations
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
A Cost-Sensitive Approach to Feature Selection in Micro-Array Data Classification
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Cooperative E-Organizations for Distributed Bioinformatics Experiments
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
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Computing methods that allow the efficient and accurate processing of experimentally gathered data play a crucial role in biological research. The aim of this paper is to present a supervised learning strategy which combines concepts stemming from coding theory and Bayesian networks for classifying and predicting pathological conditions based on gene expression data collected from micro-arrays. Specifically, we propose the adoption of the Minimum Description Length (MDL) principle as a useful heuristic for ranking and selecting relevant features. Our approach has been successfully applied to the Acute Leukemia dataset and compared with different methods proposed by other researchers.