A Comparison Study of Cost-Sensitive Learning and Sampling Methods on Imbalanced Data Sets

Abstract:

Article Preview

The classifier, built from a highly-skewed class distribution data set, generally predicts an unknown sample as the majority class much more frequently than the minority class. This is due to the fact that the aim of classifier is designed to get the highest classification accuracy. We compare three classification methods dealing with the data sets in which class distribution is imbalanced and has non-uniform misclassification cost, namely cost-sensitive learning method whose misclassification cost is embedded in the algorithm, over-sampling method and under-sampling method. In this paper, we compare these three methods to determine which one will produce the best overall classification under any circumstance. We have the following conclusion: 1. Cost-sensitive learning is suitable for the classification of imbalanced dataset. It outperforms sampling methods overall, and is more stable than sampling methods except the condition that data set is quite small. 2. If the dataset is highly skewed or quite small, over-sampling methods may be better.

Info:

Periodical:

Advanced Materials Research (Volumes 271-273)

Edited by:

Junqiao Xiong

Pages:

1291-1296

DOI:

10.4028/www.scientific.net/AMR.271-273.1291

Citation:

J. W. Zhang et al., "A Comparison Study of Cost-Sensitive Learning and Sampling Methods on Imbalanced Data Sets", Advanced Materials Research, Vols. 271-273, pp. 1291-1296, 2011

Online since:

July 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.