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A Multi-Criteria Decision Method Based on Rank Distance

Dinu L.P., Popescu M.

Fundamenta Informaticae

2008

Vol. 86, nr 1-2

79-91

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 allmpossible 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

A Generalization of the Assignment Problem, and its Application to the Rank Aggregation Problem

Manea F., Ploscaru C.

Fundamenta Informaticae

2007

Vol. 81, nr 4

459-471

In this paper we propose a generalization of the Assignment Problem. First, we describe an algorithm, based on network flow techniques, that obtains just one solution of the approached problem; further, we develop an algorithm that is able to find all the solutions. Finally, we discuss how this general form of the Assignment Problem can be applied in solving the Rank Aggregation Problem, in the case of rankings with ties.