NDLI: Wrapper-based computation and evaluation of sampling methods for imbalanced datasets

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Wrapper-based computation and evaluation of sampling methods for imbalanced datasets

Content Provider	ACM Digital Library
Author	Chawla, Nitesh V. Joshi, Ajay Hall, Lawrence O.
Abstract	Learning from imbalanced datasets presents an interesting problem both from modeling and economy standpoints. When the imbalance is large, classification accuracy on the smaller class(es) tends to be lower. In particular, when a class is of great interest but occurs relatively rarely such as cases of fraud, instances of disease, and regions of interest in largescale simulations, it is important to accurately identify it. It then becomes more costly to misclassify the interesting class. In this paper, we implement a wrapper approach that computes the amount of under-sampling and synthetic generation of the minority class examples (SMOTE) to improve minority class accuracy. The f-value serves as the evaluation function. Experimental results show the wrapper approach is effective in optimization of the composite f-value, and reduces the average cost per test example for the datasets considered. We report both average cost per test example and the cost curves in the paper. The true positive rate of the minority class increases significantly without causing a significant change in the f-value. We also obtain the lowest cost per test example, compared to any result we are aware of for the KDD Cup-99 intrusion detection data set.
Starting Page	24
Ending Page	33
Page Count	10
File Format	PDF
ISBN	1595932089
DOI	10.1145/1089827.1089830
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2005-08-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Under-sampling Wrapper Smote Imbalanced datasets Cost-sensitive learning and evaluation
Content Type	Text
Resource Type	Article

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