Soft Computing in Context-Sensitive Multidimensional Ranking.

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. [ABSTRACT FROM AUTHOR]