Title :
Multidimensional data ranking using self-organising maps and genetic algorithms
Author :
Martins, Weber ; Silva, José Carlos Meirae
Abstract :
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
Keywords :
data handling; genetic algorithms; pattern classification; probability; self-organising feature maps; blood banks inspections; genetic algorithms; genetic search; multidimensional data ranking; pattern classification; probability distribution; self-organising maps; similarity metric; Artificial neural networks; Blood; Cities and towns; Electronic mail; Genetic algorithms; Inspection; Multidimensional systems; Probability distribution; Psychology; Self organizing feature maps;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938739
