Loading...
Please wait, while we are loading the content...
Similar Documents
Mineração de regras para seleção de técnicas de agrupamento para dados de expressão gênica de câncer
| Content Provider | Semantic Scholar |
|---|---|
| Author | Nascimento, André C. A. |
| Copyright Year | 2009 |
| Abstract | Different algorithms have been used to cluster gene expression data, however there is no single algorithm that can be considered the best one independently of the data. In this work, we applied the concepts of Meta-Learning to relate features of gene expression data sets to the performance of clustering algorithms. In our context, each meta-example represents descriptive features of a gene expression data set and a label indicating the best clustering algorithm when applied to the data. A set of such meta-examples is given as input to a learning technique (the meta-learner) which is responsible to acquire knowledge relating the descriptive features and the best algorithms. In this work, we performed experiments in a case study in which a meta-learner was applied to discriminate among three competing algorithms for clustering gene expression data of cancer, as well as to extract interpretable knowledge from the experiments. The knowledge extracted by the meta-learner was useful to understand the suitability of each clustering algorithm for specific problems. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://repositorio.ufpe.br/bitstream/123456789/1812/2/acan.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
