A Multi-Criteria Decision Method Based on Rank Distance

The multi-criteria decision making process can be summarized as follows. Given a pattern d and a set C = {c(1), c(2), ... , c(m)} of all m possible categories of d, we are interested in predicting its class by using a set of n classifiers l(1), l(2), ... , l(n). Each classifier produces a ranking of categories. In this paper we propose and test a decision method which combines the rankings by using a particular method, called rank distance categorization. This method is actually based on the rank distance, a metric which was successfully used in computational linguistics and bioinformatics. We define the method, present some of its mathematical and computational properties and we test it on the digit dataset consisting of handwritten numerals ('0', ... , '9') extracted from a collection of Dutch utility maps. We compare our experimental results with other reported experiments which used the same dataset but different combining methods.