Soft computing in context-sensitive multidimensional ranking

Authors:
Weber Martins;Lauro Eugênio Guimarães Nalini;Marco Antonio Assfalk de Oliveira;Leonardo Guerra de Rezende Guedes
Affiliations:
PIRENEUS Research Group, Bloco D, Goiania, Federal University of Goias, School of Computer and Electrical Engineering, Goias, Brazil;Department of Psychology, LAEC Research Group, Bloco H, Goiania, Catholic University of Goias, Goias, Brazil;PIRENEUS Research Group, Bloco D, Goiania, Federal University of Goias, School of Computer and Electrical Engineering, Goias, Brazil;PIRENEUS Research Group, Bloco D, Goiania, Federal University of Goias, School of Computer and Electrical Engineering, Goias, Brazil
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

Citing 5
Cited 0

On the ordering conditions for self-organizing maps

Neural Computation
Learning to order things

NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Self-Organizing Maps

Self-Organizing Maps
Learning from Order Examples

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Assessing Self-Organization Using Order Metrics

IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many applications require ordering of instances represented by high dimensional vectors. Despite the reasonable quantity of papers on classification and clustering, papers on multidimensional ranking are rare. This paper expands a generic ranking procedure based on one-dimensional self-organizing maps (SOMs). The typical similarity metric is modified to a weighted Euclidean metric and automatically adjusted by a genetic search. The search goal is the best ranking that matches the desired probability distribution (provided by experts) leading to a context-sensitive metric. To ease expert agreement the technique relies on consensus about the best and worst instances. Besides the ranking task, the derived metric is also useful on reducing the number of dimensions (questionnaire items in some situations) and on modeling the data source. Promising results were achieved on the ranking of data from blood bank inspections and client segmentation in agribusiness.