Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification

<p>Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functi...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Vaibhavi Patel (19843282) (author)
مؤلفون آخرون:	Hetal Bhavsar (19843285) (author)
منشور في:	2024
الموضوعات:	Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines requiring effective strategies proposed strategy compared experimental evaluations conducted world datasets demonstrate optimizing kernel transformations challenge specifically tailored numerous classification problems classification functions toward handling imbalanced data imbalanced datasets skewed data prevalent challenge kernel trick classification tasks classification process classification capabilities svm ’ svm ), superior performance suboptimal performance practical applications novel approach minority classes method leverages majority class integrated solution existing methods established family diverse real
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Optimizing Kernel Transformations to Handle Binary Class Imbalanced Dataset Classification

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