Performance evolution of a newly developed general-use hybrid AIS-ANN system: AaA-response

Seral ÖZŞEN; Salih GÜNEŞ

Article Details

Performance evolution of a newly developed general-use hybrid AIS-ANN system: AaA-response

Authors:

Seral ÖZŞEN | Salih GÜNEŞ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 6, pp.1703-1719

Document Type:

Article

Publication Date:

5/2013

Document Language:

Turkish

Document ID (Org No.):

2000743177

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632

Abstract:

 

In this study, we have developed a nonlinear recognition system in the  artificial immune systems (AIS) field named `AaA-response (artificial neural  network (ANN)-aided AIS-response)', which is different from previous AIS  methods in that it uses a different modeling strategy in the formation of  the memory response. Because it also uses ANNs in the determination of the  correct output, it can be seen as a hybrid system that involves AIS and ANN  approaches. Unlike the other AIS methods, AaA-response uses multiple system  units (or antibodies) to form an output for a presented input. This property  gives the proposed system the ability of producing the desired output  values, other than just being a classification algorithm. That is,  AaA-response can also be used as a regression method, like ANNs, by  producing any output value for the given inputs.    The parameter analyses of the system were conducted on an artificially  generated dataset, the Chainlink dataset, and the important points in the  parameter selection were emphasized. To investigate the performance of the  system for real-world problems, the Iris dataset and Statlog Heart Disease  dataset, taken from the University of California - Irvine machine learning  repository, were used. The system, which obtained 99.33% classification  accuracy on the Iris dataset, has shown an important performance superiority  with regard to the classification accuracy to other methods in the  literature by reaching 90.37% classification accuracy for the Statlog  Heart Disease dataset.

 

In this study, we have developed a nonlinear recognition system in the  artificial immune systems (AIS) field named `AaA-response (artificial neural  network (ANN)-aided AIS-response)', which is different from previous AIS  methods in that it uses a different modeling strategy in the formation of  the memory response. Because it also uses ANNs in the determination of the  correct output, it can be seen as a hybrid system that involves AIS and ANN  approaches. Unlike the other AIS methods, AaA-response uses multiple system  units (or antibodies) to form an output for a presented input. This property  gives the proposed system the ability of producing the desired output  values, other than just being a classification algorithm. That is,  AaA-response can also be used as a regression method, like ANNs, by  producing any output value for the given inputs.    The parameter analyses of the system were conducted on an artificially  generated dataset, the Chainlink dataset, and the important points in the  parameter selection were emphasized. To investigate the performance of the  system for real-world problems, the Iris dataset and Statlog Heart Disease  dataset, taken from the University of California - Irvine machine learning  repository, were used. The system, which obtained 99.33% classification  accuracy on the Iris dataset, has shown an important performance superiority  with regard to the classification accuracy to other methods in the  literature by reaching 90.37% classification accuracy for the Statlog  Heart Disease dataset.

Article Details

Performance evolution of a newly developed general-use hybrid AIS-ANN system: AaA-response

Authors: Seral ÖZŞEN | Salih GÜNEŞ

Publisher: TUBITAK

Journal: Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 6, pp.1703-1719

Document Type: Article

Publication Date: 5/2013

Document Language: Turkish

Document ID (Org No.): 2000743177

Frequency: Bimonthly

eISSN: 1303-6203

pISSN: 1300-0632

Issues

Authors:

Seral ÖZŞEN | Salih GÜNEŞ

Publisher:

TUBITAK

Journal:

Turkish Journal of Electrical Engineering and Computer Science 2013 Vol.21 Issue 6, pp.1703-1719

Document Type:

Article

Publication Date:

5/2013

Document Language:

Turkish

Document ID (Org No.):

2000743177

Frequency:

Bimonthly

eISSN:

1303-6203

pISSN:

1300-0632