Learning from Imbalanced Data Sets

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Springer, 22 oct 2018 - 377 páginas

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge.

This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way.

This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches.

Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided.

This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

 

Páginas seleccionadas

Índice

1 Introduction to KDD and Data Science
1
2 Foundations on Imbalanced Classification
18
3 Performance Measures
47
4 CostSensitive Learning
63
5 Data Level Preprocessing Methods
79
6 AlgorithmLevel Approaches
122
7 Ensemble Learning
147
8 Imbalanced Classification with Multiple Classes
197
9 Dimensionality Reduction for Imbalanced Learning
227
10 Data Intrinsic Characteristics
253
11 Learning from Imbalanced Data Streams
278
12 Nonclassical Imbalanced Classification Problems
305
13 Imbalanced Classification for Big Data
326
14 Software and Libraries for Imbalanced Classification
351
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Otras ediciones - Ver todo

Learning from Imbalanced Data Sets
Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera
No hay ninguna vista previa disponible - 2018
Learning from Imbalanced Data Sets
Alberto Fernandez,Salvador Garcia,Mikel Galar
No hay ninguna vista previa disponible - 2019
Learning from Imbalanced Data Sets
Alberto Fernández,Salvador García,Mikel Galar,Ronaldo C. Prati,Bartosz Krawczyk,Francisco Herrera
No hay ninguna vista previa disponible - 2018

Términos y frases comunes

accuracy AdaBoost adaptation analysis applied approach authors autoencoder Bagging balanced base classifiers Big Data binary boosting class distribution class imbalance problem classification problems clustering combination Comput concept drift considered cost matrix cost-sensitive learning Data Eng Data Mining Data Science decision boundary decision tree detection ensemble learning evaluation Fernández framework function fuzzy Hadoop Hellinger distance Herrera hybrid IEEE IEEE Trans imbalance ratio imbalanced classification imbalanced data imbalanced datasets imbalanced learning imbalanced problems Intell International Conference iteration Knowl Knowledge Discovery learning algorithms learning from imbalanced Mach Machine Learning majority MapReduce metrics minority class examples minority class instances misclassification costs modified multi-class imbalanced negative Neural Networks number of instances one-class optimization output oversampling Pattern Recogn performance positive class prediction proposed Random Forest samples scenario small disjuncts SMOTE solutions Springer subset support vector machines Syst techniques training data training set undersampling values Wang weights

Información bibliográfica

TítuloLearning from Imbalanced Data Sets
AutoresAlberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, Francisco Herrera
EditorSpringer, 2018
ISBN3319980742, 9783319980744
N.º de páginas377 páginas
  
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