Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F14%3A86087620" target="_blank" >RIV/61989100:27740/14:86087620 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1142/S0129065714300071" target="_blank" >http://dx.doi.org/10.1142/S0129065714300071</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0129065714300071" target="_blank" >10.1142/S0129065714300071</a>

Result language

angličtina

Original language name

Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning

Original language description

Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues andpropose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide ran

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

<a href="/en/project/EE2.3.30.0016" target="_blank" >EE2.3.30.0016: Opportunities for young researchers</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2014

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

International Journal of Neural Systems

ISSN

0129-0657

e-ISSN

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Volume of the periodical

24

Issue of the periodical within the volume

1

Country of publishing house

SG - SINGAPORE

Number of pages

18

Pages from-to

1-18

UT code for WoS article

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EID of the result in the Scopus database

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