Affiliation:
1. National Institute of Technology, India
Abstract
Anomaly Detection is an important research domain of Pattern Recognition due to its effects of classification and clustering problems. In this paper, an anomaly detection algorithm is proposed using different primitive cost functions such as Normal Perceptron, Relaxation Criterion, Mean Square Error (MSE) and Ho-Kashyap. These criterion functions are minimized to locate the decision boundary in the data space so as to classify the normal data objects and the anomalous data objects. The authors proposed algorithm uses the concept of supervised classification, though it is very different from solving normal supervised classification problems. This proposed algorithm using different criterion functions has been compared with the accuracy of the Neural Networks (NN) in order to bring out a comparative analysis between them and discuss some advantages.
Reference17 articles.
1. Outlier detection for high dimensional data;C.Aggarwal;ACM SigmodRecordCalifornia, USA,2001
2. Time Series Clustering for Anomaly Detection Using Competitive Neural Networks
3. Unsupervised profiling methods for fraud detection.;R.Bolton;Conference on Credit Scoring and Credit Control VII,1999
4. Anomaly detection