Details
| Original language | English |
|---|---|
| Title of host publication | GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference |
| Pages | 561-568 |
| Number of pages | 8 |
| ISBN (electronic) | 9781450356183 |
| Publication status | Published - 2 Jul 2018 |
| Externally published | Yes |
| Event | 2018 Genetic and Evolutionary Computation Conference, GECCO 2018 - Kyoto, Japan Duration: 15 Jul 2018 → 19 Jul 2018 |
Publication series
| Name | GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference |
|---|
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.
Keywords
- Classification, Hierarchical decomposition, Indirect encoding
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Software
- Computer Science(all)
- Computational Theory and Mathematics
Cite this
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GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference. 2018. p. 561-568 (GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Ensembles of evolved nested dichotomies for classification
AU - Wever, Marcel
AU - Mohr, Felix
AU - Hüllermeier, Eyke
N1 - Publisher Copyright: © 2018 Copyright held by the owner/author(s).
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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.
AB - 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.
KW - Classification
KW - Hierarchical decomposition
KW - Indirect encoding
UR - http://www.scopus.com/inward/record.url?scp=85050604085&partnerID=8YFLogxK
U2 - 10.1145/3205455.3205562
DO - 10.1145/3205455.3205562
M3 - Conference contribution
AN - SCOPUS:85050604085
T3 - GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
SP - 561
EP - 568
BT - GECCO 2018 - Proceedings of the 2018 Genetic and Evolutionary Computation Conference
T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Y2 - 15 July 2018 through 19 July 2018
ER -