CORES: fusion of supervised and unsupervised training methods for a multi-class classification problem |
| |
Authors: | Igor T Podolak Adam Roman |
| |
Affiliation: | 1.Faculty of Mathematics and Computer Science, Institute of Computer Science, Jagiellonian University,Kraków,Poland |
| |
Abstract: | This paper describes in full detail a model of a hierarchical classifier (HC). The original classification problem is broken
down into several subproblems and a weak classifier is built for each of them. Subproblems consist of examples from a subset
of the whole set of output classes. It is essential for this classification framework that the generated subproblems would
overlap, i.e. some individual classes could belong to more than one subproblem. This approach allows to reduce the overall
risk. Individual classifiers built for the subproblems are weak, i.e. their accuracy is only a little better than the accuracy
of a random classifier. The notion of weakness for a multiclass model is extended in this paper. It is more intuitive than
approaches proposed so far. In the HC model described, after a single node is trained, its problem is split into several subproblems
using a clustering algorithm. It is responsible for selecting classes similarly classified. The main scope of this paper is
focused on finding the most appropriate clustering method. Some algorithms are defined and compared. Finally, we compare a
whole HC with other machine learning approaches. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|