Welcome to the InfoSci Platform
A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks

A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks

Srijan Das, Arpita Dutta, Saurav Sharma, Sangharatna Godboley
Copyright: © 2017 |Volume: 4 |Issue: 4 |Pages: 16
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522515746|DOI: 10.4018/IJRSDA.2017100101
Cite Article Cite Article

MLA

Das, Srijan, et al. "A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks." IJRSDA vol.4, no.4 2017: pp.1-16. http://doi.org/10.4018/IJRSDA.2017100101

APA

Das, S., Dutta, A., Sharma, S., & Godboley, S. (2017). A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks. International Journal of Rough Sets and Data Analysis (IJRSDA), 4(4), 1-16. http://doi.org/10.4018/IJRSDA.2017100101

Chicago

Das, Srijan, et al. "A Comparative Analysis of a Novel Anomaly Detection Algorithm with Neural Networks," International Journal of Rough Sets and Data Analysis (IJRSDA) 4, no.4: 1-16. http://doi.org/10.4018/IJRSDA.2017100101

Export Reference

Mendeley
Favorite Full-Issue Download

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.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.