An unsupervised collaborative learning method to refine classification hierarchies

Abstract

This article deals with the design of a hybrid learning system. This system integrates different kinds of unsupervised learning methods and gives a set of class hierarchies as the result. The classes in these hierarchies are very similar. The method occurrences compare their results and automatically refine them to try to make them converge towards a unique hierarchy that unifies all the results. Thus, the system decreases the importance of the initial choices made when initializing an unsupervised learning (the choice of the method and its parameters) and to solve some of the limitations of the methods used such as an imposed number of classes, a non-hierarchical result, or the size of the hierarchy.