Using Visual Interpretation of Small Ensembles in Microarray Analysis
- Resource Type
- Conference
- Authors
- Stiglic, G.; Mertik, M.; Podgorelec, V.; Kokol, P.
- Source
- 19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06) Computer-Based Medical Systems, 2006. CBMS 2006. 19th IEEE International Symposium on. :691-695 2006
- Subject
- Bioengineering
Computing and Processing
Communication, Networking and Broadcast Technologies
Robotics and Control Systems
Decision trees
Visualization
Data mining
Neural networks
Iterative algorithms
Gene expression
Data analysis
Decision making
Classification algorithms
Artificial neural networks
- Language
- ISSN
- 1063-7125
Many different classification models and techniques have been employed on gene expression data. These computational methods are in rapid and continuous evolution and there is no clear consensus on which methods are best to cope with the complex microarray data analysis. Currently ensembles of classifiers are regarded as one of the best classification techniques as they can achieve excellent classification accuracy in comparison to single classifiers methods. One of their main drawbacks is their incomprehensibility. This paper addresses the important issue of the tradeoff between accuracy and comprehensibility when building ensembles and proposes a novel visual technique for interactive interpretation of the knowledge from the small ensembles consisting of only a few decision trees. This way we can achieve better accuracy compared to single classifier, but still maintain a certain level of comprehensibility in small ensembles. The results show that our small ensembles outperform the single classifiers and still retain comprehensibility. Our study also points out that in order to take advantage of our proposed method we need more effective small ensemble building techniques.