Optimized Nearest-Neighbor Classifiers Using Generated Instances
- We present a novel approach to classification, based on a tight coupling of instancebased learning and a genetic algorithm. In contrast to the usual instance-based learning setting, we do not rely on (parts of) the given training set as the basis of a nearestneighbor classifier, but we try to employ artificially generated instances as concept prototypes. The extremely hard problem of finding an appropriate set of concept prototypes is tackled by a genetic search procedure with the classification accuracy on the given training set as evaluation criterion for the genetic fitness measure. Experiments with artificial datasets show that - due to the ability to find concise and accurate concept descriptions that contain few, but typical instances - this classification approach is considerably robust against noise, untypical training instances and irrelevant attributes. These favorable (theoretical) properties are corroborated using a number of hard real-world classification problems.
Author: | Matthias Fuchs, Andreas Abecker |
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URN: | urn:nbn:de:hbz:386-kluedo-625 |
Series (Serial Number): | LSA Report (96,2E) |
Document Type: | Preprint |
Language of publication: | English |
Year of Completion: | 1996 |
Year of first Publication: | 1996 |
Publishing Institution: | Technische Universität Kaiserslautern |
Date of the Publication (Server): | 2000/04/03 |
Tag: | Genetic Algorithm; Instance-based Learning; Nearest-Neighbor Classification |
Faculties / Organisational entities: | Kaiserslautern - Fachbereich Informatik |
DDC-Cassification: | 0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
Licence (German): | Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011 |