Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topology of strings: median string is NP-complete
Theoretical Computer Science
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Experiments with Classifier Combining Rules
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
An efficient approach for the rank aggregation problem
Theoretical Computer Science
Classifier combination based on confidence transformation
Pattern Recognition
On the syllabic similarities of romance languages
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
A Generalization of the Assignment Problem, and its Application to the Rank Aggregation Problem
Fundamenta Informaticae
A Low-complexity Distance for DNA Strings
Fundamenta Informaticae
On the Classification and Aggregation of Hierarchies with ifferent Constitutive Elements
Fundamenta Informaticae
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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.