%0 Journal Article %1 gonzalez:2003:GPEM %A Gonzalez, Fabio A. %A Dasgupta, Dipankar %D 2003 %J Genetic Programming and Evolvable Machines %K anomaly artificial detection, immune maps matching negative rule, selection, self-organizing systems, %N 4 %P 383--403 %R doi:10.1023/A:1026195112518 %T Anomaly Detection Using Real-Valued Negative Selection %V 4 %X a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organising maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.

@article{gonzalez:2003:GPEM, abstract = {a real-valued representation for the negative selection algorithm and its applications to anomaly detection. In many anomaly detection applications, only positive (normal) samples are available for training purpose. However, conventional classification algorithms need samples for all classes (e.g. normal and abnormal) during the training phase. This approach uses only normal samples to generate abnormal samples, which are used as input to a classification algorithm. This hybrid approach is compared against an anomaly detection technique that uses self-organising maps to cluster the normal data sets (samples). Experiments are performed with different data sets and some results are reported.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Gonzalez, Fabio A. and Dasgupta, Dipankar}, biburl = {https://www.bibsonomy.org/bibtex/2ede5d500014dd824ca12500cf7cfbc63/brazovayeye}, doi = {doi:10.1023/A:1026195112518}, interhash = {2a148399c4f20906937a573e610fc0de}, intrahash = {ede5d500014dd824ca12500cf7cfbc63}, issn = {1389-2576}, journal = {Genetic Programming and Evolvable Machines}, keywords = {anomaly artificial detection, immune maps matching negative rule, selection, self-organizing systems,}, month = {December}, notes = {Special issue on artificial immune systems Article ID: 5144849}, number = 4, pages = {383--403}, timestamp = {2008-06-19T17:40:27.000+0200}, title = {Anomaly Detection Using Real-Valued Negative Selection}, volume = 4, year = 2003 }