Support Vector Data Description
Machine Learning
One-Class Classification by Combining Density and Class Probability Estimation
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Using Imputation Techniques to Help Learn Accurate Classifiers
ICTAI '08 Proceedings of the 2008 20th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Density weighted support vector data description
Expert Systems with Applications: An International Journal
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One-Class Classifiers build classification models in the absence of negative examples, which makes it harder to estimate the class boundary. The predictive accuracy of one-class classifiers can be exacerbated by the presence of missing data in the positive class. In this paper, we propose two approaches based on Bayesian Multiple Imputation (BMI) for imputing missing data in the one-class classification framework called Averaged BMI and Ensemble BMI. We test and compare our approaches against the common method of Mean imputation and Expectation Maximization on several datasets. Our preliminary experiments suggest that as the missingness in the data increases, our proposed imputation approaches can do better on some data sets.