The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study

2006 ◽  
Author(s):  
Xu-ying Liu ◽  
Zhi-hua Zhou
Keyword(s):  
Empirical Study ◽  
Class Imbalance ◽  
Cost Sensitive Learning

A Measure Optimized Cost-Sensitive Learning Framework for Imbalanced Data Classification

2014 ◽  
pp. 48-75 ◽  
Author(s):  
Peng Cao ◽  
Osmar Zaiane ◽  
Dazhe Zhao
Keyword(s):  
Real World ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Support Vector ◽  
Feature Subset ◽  
Cost Sensitive Learning ◽  
Intrinsic Parameters ◽  
Real World Problem ◽  
Benchmark Datasets ◽  
Feed Forward Neural Networks

Class imbalance is one of the challenging problems for machine-learning in many real-world applications. Many methods have been proposed to address and attempt to solve the problem, including sampling and cost-sensitive learning. The latter has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. There are also other factors that influence the performance of the classification including the input feature subset and the intrinsic parameters of the classifier. This chapter presents an effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive learning directly to improve the performance of classification by simultaneously optimizing the best pair of feature subset, intrinsic parameters, and misclassification cost parameter. The optimization is based on Particle Swarm Optimization (PSO). The authors use two different common methods, support vector machine and feed forward neural networks, to evaluate the proposed framework. Experimental results on various standard benchmark datasets with different ratios of imbalance and a real-world problem show that the proposed method is effective in comparison with commonly used sampling techniques.

An Empirical Study of Boosting Methods on Severely Imbalanced Data

2014 ◽  
Vol 513-517 ◽  
pp. 2510-2513 ◽  
Author(s):  
Xu Ying Liu
Keyword(s):  
Empirical Study ◽  
Real World ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Real World Applications ◽  
Under Sampling ◽  
The Difference ◽  
Imbalance Learning ◽  
Class Imbalance Learning ◽  
F Measure

Nowadays there are large volumes of data in real-world applications, which poses great challenge to class-imbalance learning: the large amount of the majority class examples and severe class-imbalance. Previous studies on class-imbalance learning mainly focused on relatively small or moderate class-imbalance. In this paper we conduct an empirical study to explore the difference between learning with small or moderate class-imbalance and learning with severe class-imbalance. The experimental results show that: (1) Traditional methods cannot handle severe class-imbalance effectively. (2) AUC, G-mean and F-measure can be very inconsistent for severe class-imbalance, which seldom appears when class-imbalance is moderate. And G-mean is not appropriate for severe class-imbalance learning because it is not sensitive to the change of imbalance ratio. (3) When AUC and G-mean are evaluation metrics, EasyEnsemble is the best method, followed by BalanceCascade and under-sampling. (4) A little under-full balance is better for under-sampling to handle severe class-imbalance. And it is important to handle false positives when design methods for severe class-imbalance.

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