Cost-sensitive learning methods for imbalanced data
N Thai-Nghe, Z Gantner… - The 2010 International …, 2010 - ieeexplore.ieee.org
Class imbalance is one of the challenging problems for machine learning algorithms. When
learning from highly imbalanced data, most classifiers are overwhelmed by the majority …
learning from highly imbalanced data, most classifiers are overwhelmed by the majority …
An optimized cost-sensitive SVM for imbalanced data learning
Class imbalance is one of the challenging problems for machine learning in many real-world
applications. Cost-sensitive learning has attracted significant attention in recent years to …
applications. Cost-sensitive learning has attracted significant attention in recent years to …
[PDF][PDF] C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling
C Drummond, RC Holte - … on learning from imbalanced datasets II, 2003 - eiti.uottawa.ca
This paper takes a new look at two sampling schemes commonly used to adapt machine
learning algorithms to imbalanced classes and misclassification costs. It uses a performance …
learning algorithms to imbalanced classes and misclassification costs. It uses a performance …
SVMs modeling for highly imbalanced classification
Traditional classification algorithms can be limited in their performance on highly
unbalanced data sets. A popular stream of work for countering the problem of class …
unbalanced data sets. A popular stream of work for countering the problem of class …
Cost-sensitive learning
Cost-sensitive learning is an aspect of algorithm-level modifications for class imbalance.
Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a …
Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a …
Addressing the classification with imbalanced data: open problems and new challenges on class distribution
Classifier learning with datasets which suffer from imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …
important problem in data mining. This issue occurs when the number of examples …
A novel ensemble method for classifying imbalanced data
The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …
performance of many standard learning algorithms, and have attracted a great deal of …
Comparing the behavior of oversampling and undersampling approach of class imbalance learning by combining class imbalance problem with noise
Class imbalance learning is a recent topic, which helps us to detect the classes from
unbalanced datasets. In various real scenarios, where we need to find the exceptional cases …
unbalanced datasets. In various real scenarios, where we need to find the exceptional cases …
Learning pattern classification tasks with imbalanced data sets
GH Nguyen, A Bouzerdoum, SL Phung - Pattern recognition, 2009 - books.google.com
This chapter is concerned with the class imbalance problem, which has been recognized as
a crucial problem in machine learning and data mining. The problem occurs when there are …
a crucial problem in machine learning and data mining. The problem occurs when there are …
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics
Class imbalance is among the most persistent complications which may confront the
traditional supervised learning task in real-world applications. The problem occurs, in the …
traditional supervised learning task in real-world applications. The problem occurs, in the …