Multidimensional data ranking using self-organising maps and genetic algorithms
| Autor: | Wellington Santos Martins, J. Carlos Meira e Silva |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Group method of data handling
business.industry Context (language use) computer.software_genre Machine learning Ranking (information retrieval) Ranking SVM Component (UML) Metric (mathematics) Probability distribution Data mining Artificial intelligence Cluster analysis business computer Mathematics |
| Zdroj: | IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222). |
| DOI: | 10.1109/ijcnn.2001.938739 |
| Popis: | There are applications that require ordered instances modeled by high dimensional vectors. Despite the reasonable quantity of papers on the areas of classification and clustering and its crescent importance, papers on ranking are rare. Usual solutions are not generic and demand expert knowledge on the specification of the weight of each component and, therefore, the definition of a ranking function. This paper proposes a generic procedure for ranking, based on 1D self-organizing maps (SOMs). Additionally, the similarity metric used by SOM is modified and automatically adjusted to the context by a genetic search. This process seeks for the best ranking that marches the desired probability distribution provided by the specialist expectation. Promising results were achieved on the ranking of data from blood banks inspections. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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