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Learning from Imbalanced Data Sets
Cost-Sensitive Learning
other
Author(s):
Alberto Fernández
,
Salvador García
,
Mikel Galar
,
Ronaldo C. Prati
,
Bartosz Krawczyk
,
Francisco Herrera
Publication date
(Online):
October 23 2018
Publisher:
Springer International Publishing
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Sex and gender-sensitive medicine
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An introduction to ROC analysis
Tom Fawcett
(2006)
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Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhi-Hua Zhou
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Xu-Ying Liu
(2006)
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Applying Support Vector Machines to Imbalanced Datasets
Rehan Akbani
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Stephen S. Kwek
,
Nathalie Japkowicz
(2004)
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Book Chapter
Publication date (Print):
2018
Publication date (Online):
October 23 2018
Pages
: 63-78
DOI:
10.1007/978-3-319-98074-4_4
SO-VID:
0a3997c1-b56f-46f3-ba54-9e3f097a52d4
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Book chapters
pp. 1
Introduction to KDD and Data Science
pp. 19
Foundations on Imbalanced Classification
pp. 47
Performance Measures
pp. 63
Cost-Sensitive Learning
pp. 79
Data Level Preprocessing Methods
pp. 123
Algorithm-Level Approaches
pp. 147
Ensemble Learning
pp. 197
Imbalanced Classification with Multiple Classes
pp. 253
Data Intrinsic Characteristics
pp. 279
Learning from Imbalanced Data Streams
pp. 305
Non-classical Imbalanced Classification Problems
pp. 327
Imbalanced Classification for Big Data
pp. 351
Software and Libraries for Imbalanced Classification
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