PT Unknown
AU Santiago Segui
   Laura Igual
   Jordi Vitria
TI Weighted Bagging for Graph based One-Class Classifiers
BT 9th International Workshop on Multiple Classifier Systems
PY 2010
BP 1
EP 10
VL 5997
DI 10.1007/978-3-642-12127-2_1
AB Most conventional learning algorithms require both positive and negative training data for achieving accurate classification results. However, the problem of learning classifiers from only positive data arises in many applications where negative data are too costly, difficult to obtain, or not available at all. Minimum Spanning Tree Class Descriptor (MST_CD) was presented as a method that achieves better accuracies than other one-class classifiers in high dimensional data. However, the presence of outliers in the target class severely harms the performance of this classifier. In this paper we propose two bagging strategies for MST_CD that reduce the influence of outliers in training data. We show the improved performance on both real and artificially contaminated data.
ER