Improving the classification performance on imbalanced data sets via new hybrid parameterisation model

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50016456" target="_blank" >RIV/62690094:18450/21:50016456 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1319157818312229" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1319157818312229</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.jksuci.2019.04.009" target="_blank" >10.1016/j.jksuci.2019.04.009</a>

Result language
angličtina
Original language name
Improving the classification performance on imbalanced data sets via new hybrid parameterisation model
Original language description
The aim of this work is to analyse the performance of the new proposed hybrid parameterisation model in handling problematic data. Three types of problematic data will be highlighted in this paper: i) big data set, ii) uncertain and inconsistent data set and iii) imbalanced data set. The proposed hybrid model is an integration of three main phases which consist of the data decomposition, parameter reduction and parameter selection phases. Three main methods, which are soft set and rough set theories, were implemented to reduce and to select the optimised parameter set, while a neural network was used to classify the optimised data set. This proposed model can process a data set that might contain uncertain, inconsistent and imbalanced data. Therefore, one additional phase, data decomposition, was introduced and executed after the pre-processing task was completed in order to manage the big data issue. Imbalanced data sets were used to evaluate the capability of the proposed hybrid model in handling problematic data. The experimental results demonstrate that the proposed hybrid model has the potential to be implemented with any type of data set in a classification task, especially with complex data sets. © 2019 The Authors
Czech name
—
Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
Journal of King Saud university - computer and information sciences
ISSN
1319-1578
e-ISSN
—
Volume of the periodical
33
Issue of the periodical within the volume
7
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
11
Pages from-to
787-797
UT code for WoS article
000688355700003
EID of the result in the Scopus database
2-s2.0-85065134291

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