Abstract
Analyzing DNA microarray data pose a serious challenge because of their large number of features (genes) and relatively small number of samples. Extracting features, those have predictive capability for classifying these huge datasets
demands appropriate approaches like feature reduction and identifying optimal set of genes. In this paper along with conventional statistical methods like filtering the dataset to reduce the number of features, one additional approach of evaluating correlation between the classes for each feature is performed. Proposed approach yields higher classification accuracy for both Acute Lymphoblastic (ALL) and High Grade Glioma cancer dataset than using only traditional statistical filtering methods.
demands appropriate approaches like feature reduction and identifying optimal set of genes. In this paper along with conventional statistical methods like filtering the dataset to reduce the number of features, one additional approach of evaluating correlation between the classes for each feature is performed. Proposed approach yields higher classification accuracy for both Acute Lymphoblastic (ALL) and High Grade Glioma cancer dataset than using only traditional statistical filtering methods.
Original language | English |
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Title of host publication | 2012 International Conference on Biomedical Engineering (ICoBE) |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Pages | 319-321 |
Number of pages | 3 |
ISBN (Electronic) | 9781457719912 |
ISBN (Print) | 9781457719905 |
DOIs | |
Publication status | Published - 27 Feb 2012 |
Externally published | Yes |
Event | 2012 International Conference on Biomedical Engineering (ICoBE) - Penang, Malaysia Duration: 27 Feb 2012 → 28 Feb 2012 |
Conference
Conference | 2012 International Conference on Biomedical Engineering (ICoBE) |
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Country/Territory | Malaysia |
City | Penang |
Period | 27/02/12 → 28/02/12 |
Keywords
- DNA microarray data
- correlation
- feature selection
- classification