Combining classifiers using trained fuser -- analytical and experimental results

Title:

Combining classifiers using trained fuser -- analytical and experimental results

Creator:

Wozniak, Michal and Zmyslony , Marcin

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:278d8758-0031-4116-a73d-75191d395f3f
uuid:278d8758-0031-4116-a73d-75191d395f3f

Subject:

Pattern recognition, multiple classifier system, trained fuser, and neural networks

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Combining pattern recognition is a promising direction in designing effective classifiers. There are several approaches to collective decision-making, including quite popular voting methods where the decision is a combination of individual classifiers' outputs. The article focuses on the problem of fuser design which uses discriminants of individual classifiers to make a decision. We present taxonomy of proposed fusers and discuss some of their properties. We focus on the fuser which uses weights dependent on classifier and class number, because of a pretty low computational cost of its training. We formulate the problem of fuser learning as an optimization task and propose a solver which has its origin in neural computations. The quality of proposed learning algorithm was evaluated on the basis of several computer experiments, which were carried out on five benchmark datasets and their results confirm the quality of proposed concept.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2010 Volume:20 | Number:7

Harvested from:

CDK

Metadata only:

false