A novel-weighted rough set-based meta learning for ozone day prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092813" target="_blank" >RIV/61989100:27240/14:86092813 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092813

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

A novel-weighted rough set-based meta learning for ozone day prediction

Popis výsledku v původním jazyce

Nowadays, classifier combination methodsreceives great attention from machine learning researchers. It is a powerful tool to improve the accuracy of classifiers. This approach has become increasingly interesting, especially for real-world problems, whichare often characterized by their imbalanced nature. The unbalanced distribution of data leads to poor performance of most of the conventional machine learning techniques. In this paper, we propose a novel weighted rough set as a Meta classifier framework for 14 classifiers to find the smallest and optimal ensemble, which maximize the overall ensemble accuracy. We propose a new entropy-based method to compute the weight of each classifier. Each classifier assigns a weight based on its contribution to classification accuracy. Thanks to the powerful reduct technique in rough set, this guarantees high diversity of the produced reduct ensembles. The higher diversity between the core classifiers has a positive impact on the performance of mi

Název v anglickém jazyce

A novel-weighted rough set-based meta learning for ozone day prediction

Popis výsledku anglicky

Nowadays, classifier combination methodsreceives great attention from machine learning researchers. It is a powerful tool to improve the accuracy of classifiers. This approach has become increasingly interesting, especially for real-world problems, whichare often characterized by their imbalanced nature. The unbalanced distribution of data leads to poor performance of most of the conventional machine learning techniques. In this paper, we propose a novel weighted rough set as a Meta classifier framework for 14 classifiers to find the smallest and optimal ensemble, which maximize the overall ensemble accuracy. We propose a new entropy-based method to compute the weight of each classifier. Each classifier assigns a weight based on its contribution to classification accuracy. Thanks to the powerful reduct technique in rough set, this guarantees high diversity of the produced reduct ensembles. The higher diversity between the core classifiers has a positive impact on the performance of mi

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Acta Polytechnica Hungarica

ISSN

1785-8860

e-ISSN

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Svazek periodika

11

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

HU - Maďarsko

Počet stran výsledku

20

Strana od-do

59-78

Kód UT WoS článku

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EID výsledku v databázi Scopus

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