Machine Learning - Special issue on learning with probabilistic representations
Pruning Improves Heuristic Search for Cost-Sensitive Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Inducing Cost-Sensitive Trees via Instance Weighting
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Test-Cost Sensitive Naive Bayes Classification
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Artificial Intelligence in Medicine
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Learning bayesian classifiers from gene-expression microarray data
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
A Framework for Multi-class Learning in Micro-array Data Analysis
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
An SOA-based model for the integrated provisioning of cloud and grid resources
Advances in Software Engineering
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In analyzing gene expression data from micro-array, a major challenge is the definition of a feature selection criterion to judge the goodness of a subset of features with respect to a particular classification model. This paper presents a cost-sensitive approach feature selection that focuses on two fundamental requirements: (1) the quality of the features in order to promote the classifier accuracy and (2) the cost of computation due to the complexity that occurs during training and testing the classifier. The paper describes the approach in detail and includes a case study for a publicly available micro-array dataset. Results show that the proposed process yields state-of-art performance and uses only a small fraction of features that are generally used in competitive approaches on the same dataset.