Classifier Learning for Imbalanced Data

Name: Classifier Learning for Imbalanced Data
Brand: VDM Verlag Dr. Mueller E.K.
SKU: 9783836492232
Price: 68.74 EUR
Availability: InStock

A Comparison of kNN, SVM, and Decision Tree Learning

Автор: Jörg Mennicke

Език:

Корици: С меки корици

Издател: VDM Verlag Dr. Mueller E.K.

Наличност: Външен склад

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68.74 € 134.45 лв

This work discusses the theoretical abilities ofthree commonly used classifier learning methods ando...

Информация за книгата

Автор

Jörg Mennicke

Език

Английски език

Корици

Книга - С меки корици

Издадена

2008

страници

184

EAN

9783836492232

ISBN

3836492237

Enbook ID

07008751

Издател

VDM Verlag Dr. Mueller E.K.

Теглоt

254

Размери

152 x 229 x 10

Пълно описание

This work discusses the theoretical abilities ofthree commonly used classifier learning methods andoptimization techniques to cope with characteristicsof real-world classification problems, morespecifically varying misclassification costs,imbalanced data sets and varying degrees of hardnessof class boundaries.From these discussions a universally applicableoptimization framework is derived that successfullycorrects the error-based inductive bias of classifierlearning methods on image data within the domain ofmedical diagnosis.The framework was designed considering several pointsfor improvement of common optimization techniques,such as the modification of the optimizationprocedure for inducer-specific parameters, themodification of input data by an arcing algorithm,and the combination of classifiers according tolocally-adaptive, cost-sensitive voting schemes.The framework is designed to make the learningprocess cost-sensitive and to enforce more balancedmisclassification costs between classes. Results onthe evaluated domain are promising, while furtherimprovements can be expected after some modificationsto the framework.

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Classifier Learning for Imbalanced Data

A Comparison of kNN, SVM, and Decision Tree Learning

Информация за книгата

Пълно описание

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