Support Vector Machine incorporated with feature discrimination
Expert Systems with Applications: An International Journal
Distributed learning with data reduction
Transactions on computational collective intelligence IV
Using a boosted tree classifier for text segmentation in hand-annotated documents
Pattern Recognition Letters
Research of neural network classifier based on FCM and PSO for breast cancer classification
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Distributed customer behavior prediction using multiplex data: A collaborative MK-SVM approach
Knowledge-Based Systems
BRACID: a comprehensive approach to learning rules from imbalanced data
Journal of Intelligent Information Systems
3D modeling of multiple-object scenes from sets of images
Pattern Recognition
GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems
Applied Soft Computing
IIvotes ensemble for imbalanced data
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed vector spaces or lack of information. Common approaches for dealing with the class imbalance problem involve modifying the data distribution or modifying the classifier. In this work, we choose to use a combination of both approaches. We use support vector machines with soft margins as the base classifier to solve the skewed vector spaces problem. We then counter the excessive bias introduced by this approach with a boosting algorithm. We found that this ensemble of SVMs makes an impressive improvement in prediction performance, not only for the majority class, but also for the minority class.