Applying both positive and negative selection to supervised learning for anomaly detection

Hang, Xiaoshu and Dai, Honghua 2005, Applying both positive and negative selection to supervised learning for anomaly detection, in GECCO 2005 : Genetic and Evolutionary Computation Conference, June 25-29, 2005 (Saturday-Wednesday) Washington, D.C., USA, Association for Computing Machinery, New York, N.Y., pp. 345-352.

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Title Applying both positive and negative selection to supervised learning for anomaly detection
Author(s) Hang, Xiaoshu
Dai, Honghua
Conference name Genetic and Evolutionary Computation Conference (7th : 2005 : Washington, D.C.)
Conference location Washington DC, USA
Conference dates 25-29 June 2005
Title of proceedings GECCO 2005 : Genetic and Evolutionary Computation Conference, June 25-29, 2005 (Saturday-Wednesday) Washington, D.C., USA
Editor(s) Beyer, Hans-Georg
O'Reilly, U.
Publication date 2005
Conference series Genetic and Evolutionary Computation Conference
Start page 345
End page 352
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Keyword(s) artificial immune system
supervised learning
anomaly detection
positive selection
negative selection
Summary This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the normal class via co-evolutionary genetic algorithm, which is inspired from the positive selection, and then generates synthetic samples of the anomaly class, which is based on the negative selection in the immune system. Two algorithms about synthetic generation of the anomaly class are proposed. One deals with data sets containing a few anomalous samples; while the other deals with data sets containing no anomalous samples at all. The experimental results on some benchmark data sets from UCI data set repertory show that the detection rate is improved evidently, accompanied by a slight increase in false alarm rate via introducing novel synthetic samples of the anomaly class. The advantages of our method are the increased ability of classifiers in identifying both previously known and innovative anomalies, and the maximal degradation of overfitting phenomenon.
ISBN 1595930108
9781595930101
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2005 ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005837

Document type: Conference Paper
Collection: School of Information Technology
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