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Applying Cost-Sensitive Extreme Learning Machine and Dissimilarity Integration to Gene Expression Data Classification
Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-E...
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| 出版年: | Comput Intell Neurosci |
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| 主要な著者: | , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Hindawi Publishing Corporation
2016
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5011754/ https://ncbi.nlm.nih.gov/pubmed/27642292 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1155/2016/8056253 |
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