Abstract
We believe the great interpretability of fuzzy models allow fuzzy-based methods to play a very important role in Microarray gene expression data analysis, but the advantages offered by fuzzy-based techniques in this application have not yet been fully explored in the literature. In this paper, we construct Multi-Objective Evolutionary Algorithms based Interpretable Fuzzy (MOEAIF) models for microarray gene expression data analysis. Our novel fuzzy models can significantly decrease the model complexity, and automatically balance the accuracy and interpretability of the models. The experimental studies have shown that relatively simple and small fuzzy rule bases, with satisfactory classification performance, have been successful found for challenging microarray gene expression datasets. Cancer gene expression data, fuzzy rule-based.
| Original language | English |
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| Title of host publication | Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 |
| Publisher | IEEE |
| Pages | 308-313 |
| Number of pages | 6 |
| ISBN (Print) | 9781424483075 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 - Hong Kong, China Duration: 18 Dec 2010 → 21 Dec 2010 |
Conference
| Conference | 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010 |
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| Country/Territory | China |
| City | Hong Kong |
| Period | 18/12/10 → 21/12/10 |
Keywords
- Cancer gene expression data
- Evolutionary algorithms
- Feature selection
- Fuzzy rule-based systems
- Microarray data analysis
- Model interpretability
- Multi-objective optimisation
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
