The new upper bound on the probability of error in binary tree classifier with fuzzy information

Title:

The new upper bound on the probability of error in binary tree classifier with fuzzy information

Creator:

Burduk , Robert

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:9cd88631-c9ed-425c-bfde-ed18d546aa48
uuid:9cd88631-c9ed-425c-bfde-ed18d546aa48

Subject:

Binary tree classifier, probability of error, fuzzy observations, and Bayes rule

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

The paper considers the mixture of randomness and fuzziness in a binary tree classifier. This model of classification is based on fuzzy observations, the randomness of classes and the Bayes rule. In this work, we present a new upper bound on the probability of error in a binary tree classifier. The obtained error for fuzzy observations is compared with the case when observations are not fuzzy, as a difference of errors. Additionally, the obtained results are compared with the bound on the probability of error based on information energy of fuzzy events. For interior nodes of decision tree, the new bound is twice as precise as the bound based on information energy.

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