Ensembles of evolved nested dichotomies for classification
Abstract
In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance.
Details
- External Organisation(s)
-
Heinz Nixdorf Institute
Paderborn University
- Type
- Conference contribution
- Pages
- 561-568
- No. of pages
- 8
- Publication date
- 02.07.2018
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- Computer Science Applications, Software, Computational Theory and Mathematics
- Electronic version(s)
-
https://doi.org/10.1145/3205455.3205562 (Access:
Closed
)
