Machine Learning
Comparing Pure Parallel Ensemble Creation Techniques Against Bagging
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Microarray data mining with visual programming
Bioinformatics
Using Visual Interpretation of Small Ensembles in Microarray Analysis
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Theory and scope of exact representation extraction from feed-forward networks
Cognitive Systems Research
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Many advanced machine learning and statistical methods have recently been employed in classification of gene expression measurements. Although many of these methods can achieve high accuracy, they generally lack comprehensibility of the classification process. In this paper a new method for interpretation of small ensembles of classifiers is used on gene expression data from real-world dataset. It was shown that interactive interpretation systems that were developed for classical machine learning problems also give a great range of possibilities for the scientists in the bioinformatics field. Therefore we chose a gene expression dataset discriminating three types of Leukemia as a testbed for the proposed Visual Interpretation of Small Ensembles (VISE) tool. Our results show that using the accuracy of ensembles and adding comprehensibility gains not only accurate but also results that can possibly represent new knowledge on specific gene functions.