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

Result code in 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>
Alternative codes found
RIV/61989100:27740/14:86092813
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
A novel-weighted rough set-based meta learning for ozone day prediction
Original language description
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
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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