Könyv Classifier Learning for Imbalanced Data Jörg Mennicke

Classifier Learning for Imbalanced Data

A Comparison of kNN, SVM, and Decision Tree Learning

Szerző: Jörg Mennicke
Nyelv: Angol
Kötés: Puha kötésű
Elérhetőség: Beszállítói készleten
Küldés 9-15 napon belül
26 272 Ft
This work discusses the theoretical abilities ofthree commonly used classifier learning methods ando...

Információk a könyvről

Szerző
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2008
oldal
184
EAN
9783836492232
ISBN
3836492237
Enbook ID
07008751
Súly
254
Méretek
152 x 229 x 10

Teljes leírás

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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