Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F22%3A50020126" target="_blank" >RIV/62690094:18450/22:50020126 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICOCO56118.2022.10031922" target="_blank" >http://dx.doi.org/10.1109/ICOCO56118.2022.10031922</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICOCO56118.2022.10031922" target="_blank" >10.1109/ICOCO56118.2022.10031922</a>
Alternative languages
Result language
angličtina
Original language name
Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest
Original language description
Data mining is a knowledge discovery of the data that extracts and discovers patterns and relationships to predict outcomes. Class imbalance is one of the obstacles that can drive misclassification. The class imbalance affected the result of classification machine learning. The classification technique can divide the data into the given class target. This research focuses on four pre-processing methods: SMOTE, Spread Subsample, Class Balancer, and Resample. These methods can help to clean the data before undergoing the classification techniques. Resample shows the best result for solving the imbalance problem with 41.321 for Mean and Standard Deviation, 64.101. Besides, this research involves six classification techniques: Naïve Bayes, BayesNet, Random Forest, Random Tree, Logistics, and Multilayer Perceptron. Indeed, the combination of Resample and Random Forest has the best result of Precision, 0.941, and ROC Area is 0.983.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 IEEE International Conference on Computing (ICOCO)
ISBN
978-1-66548-996-6
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
316-323
Publisher name
IEEE
Place of publication
New Jersey
Event location
Kota Kinabalu, Malaysia
Event date
Nov 14, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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