Abstract
Feature (Gene) selection is a frequently used preprocessing technology for successful cancer classification task in microarray gene expression data analysis. Widely used gene selection approaches are mainly focused on the filter methods. Filter methods are usually considered to be very effective and efficient for high-dimensional data. This paper reviews the existing filter methods, and shows the performance of the representative algorithms on microarray data by extensive experimental study. Surprisingly, the experimental results show that filter methods are not very effective on microarray data. We analyze the cause of the result and provide the basic ideas for potential solutions.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 97-100 |
| Number of pages | 4 |
| ISBN (Print) | 9781424416042 |
| DOIs | |
| State | Published - 2007 |
| Event | 2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW - San Jose, CA, United States Duration: 2 Nov 2007 → 4 Nov 2007 |
Publication series
| Name | Proceedings - 2007 IEEE International Conference on Bioinformaticsand Biomedicine Workshops, BIBMW |
|---|
Conference
| Conference | 2007 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW |
|---|---|
| Country/Territory | United States |
| City | San Jose, CA |
| Period | 2/11/07 → 4/11/07 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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