Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning in the presence of concept drift and hidden contexts
Machine Learning
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Knowledge Discovery and Data Mining: The Info-Fuzzy Network (Ifn) Methodology
Knowledge Discovery and Data Mining: The Info-Fuzzy Network (Ifn) Methodology
Computer
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
An Approach to Novelty Detection Applied to the Classification of Image Regions
IEEE Transactions on Knowledge and Data Engineering
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
A Unifying Framework for Detecting Outliers and Change Points from Time Series
IEEE Transactions on Knowledge and Data Engineering
Online classification of nonstationary data streams
Intelligent Data Analysis
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
Change detection in time series data using wavelet footprints
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Fault diagnosis model based on Gaussian support vector classifier machine
Expert Systems with Applications: An International Journal
Car assembly line fault diagnosis based on modified support vector classifier machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Approximate polytope ensemble for one-class classification
Pattern Recognition
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Novelty detection, also referred to as one-class classification, is the process of detecting 'abnormal' behavior in a system by learning the 'normal' behavior. Novelty detection has been of particular interest to researchers in domains where it is difficult or expensive to find examples of abnormal behavior (such as in medical/equipment diagnosis and IT network surveillance). Effective representation of normal data is of primary interest in pursuing one-class classification. While the literature offers several methods for one-class classification, very few methods can support representation of non-stationary classes without making stringent assumptions about the class distribution. This paper proposes a one-class classification method for non-stationary classes using a modified support vector machine and an efficient online version for reducing computational time. The presented method is applied to several simulated datasets and actual data from a drilling machine. In addition, we present comparison results with other methods that demonstrate its superior performance.