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GECCO
2005
Springer

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

9 years 2 months ago
Applying both positive and negative selection to supervised learning for anomaly detection
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 ...
Xiaoshu Hang, Honghua Dai
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where GECCO
Authors Xiaoshu Hang, Honghua Dai
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