C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
The common order-theoretic structure of version spaces and ATMSs
Artificial Intelligence
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
CAEP: Classification by Aggregating Emerging Patterns
DS '99 Proceedings of the Second International Conference on Discovery Science
Bioinformatics Adventures in Database Research
ICDT '03 Proceedings of the 9th International Conference on Database Theory
From informatics to bioinformatics
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Feature space transformation and decision results interpretation
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
Incremental Maintenance on the Border of the Space of Emerging Patterns
Data Mining and Knowledge Discovery
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Structural geography of the space of emerging patterns
Intelligent Data Analysis
Subgroup discovery techniques and applications
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Relevancy in constraint-based subgroup discovery
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
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Easily comprehensible ways of capturing main differences between two classes of data are investigated in this paper. In addition to examining individual differences, we also consider their neighbourhood. The new concepts are applied to three gene expression datasets to discover diagnostic gene groups. Based on the idea of prediction by collective likelihoods (PCL), a new method is proposed to classify testing samples. Its performance is competitive to several state-of-the-art algorithms.