Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název statě ve sborníku

2022 IEEE International Conference on Computing (ICOCO)

ISBN

978-1-66548-996-6

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

316-323

Název nakladatele

IEEE

Místo vydání

New Jersey

Místo konání akce

Kota Kinabalu, Malaysia

Datum konání akce

14. 11. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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