Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Fault classifier of rotating machinery based on weighted support vector data description
Expert Systems with Applications: An International Journal
A support vector domain method for change detection in multitemporal images
Pattern Recognition Letters
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
A survey of recent trends in one class classification
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
A SVDD approach of fuzzy classification for analog circuit fault diagnosis with FWT as preprocessor
Expert Systems with Applications: An International Journal
A differentiated one-class classification method with applications to intrusion detection
Expert Systems with Applications: An International Journal
Density-Induced Support Vector Data Description
IEEE Transactions on Neural Networks
Bayesian multiple imputation approaches for one-class classification
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Hi-index | 12.05 |
One-class classification (OCC) has received a lot of attention because of its usefulness in the absence of statistically-representative non-target data. In this situation, the objective of OCC is to find the optimal description of the target data in order to better identify outlier or non-target data. An example of OCC, support vector data description (SVDD) is widely used for its flexible description boundaries without the need to make assumptions regarding data distribution. By mapping the target dataset into high-dimensional space, SVDD finds the spherical description boundary for the target data. In this process, SVDD considers only the kernel-based distance between each data point and the spherical description, not the density distribution of the data. Therefore, it may happen that data points in high-density regions are not included in the description, decreasing classification performance. To solve this problem, we propose a new SVDD introducing the notion of density weight, which is the relative density of each data point based on the density distribution of the target data using the k-nearest neighbor (k-NN) approach. Incorporating the new weight into the search for an optimal description using SVDD, this new method prioritizes data points in high-density regions, and eventually the optimal description shifts to these regions. We demonstrate the improved performance of the new SVDD by using various datasets from the UCI repository.