Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-12127-2_1 | MCS |
Keywords | Field | DocType |
training data,improved performance,negative data,conventional learning algorithm,contaminated data,one-class classifier,minimum spanning tree class,high dimensional data,accurate classification result,positive data,weighted bagging,negative training data,minimum spanning tree | Training set,Graph,Clustering high-dimensional data,Pattern recognition,Computer science,Outlier,Artificial intelligence,Classifier (linguistics),Machine learning,Kernel density estimation,Minimum spanning tree | Conference |
Volume | ISSN | ISBN |
5997 | 0302-9743 | 3-642-12126-8 |
Citations | PageRank | References |
9 | 0.51 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Santi Seguí | 1 | 85 | 9.11 |
| Laura Igual | 2 | 266 | 18.41 |
| Jordi Vitrià | 3 | 737 | 98.14 |