Brunel University Research Archive (BURA) >
Schools >
School of Engineering and Design >
School of Engineering and Design Theses >

Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6826

Title: Artificial immune systems based committee machine for classification application
Authors: Al-Enezi, Jamal
Advisors: Abbod, MF
Alsharhan, S
Keywords: Ensemble model
Clonal selection
Negative selection
Artificial immune networks
Neuro-fuzzy detector
Publication Date: 2012
Publisher: Brunel University School of Engineering and Design PhD Theses
Abstract: A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion.
Description: This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.
URI: http://bura.brunel.ac.uk/handle/2438/6826
Appears in Collections:School of Engineering and Design Theses
Electronic and Computer Engineering

Files in This Item:

File Description SizeFormat
FulltextThesis.pdf3.16 MBAdobe PDFView/Open

Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.

 


Library (c) Brunel University.    Powered By: DSpace
Send us your
Feedback. Last Updated: September 14, 2010.
Managed by:
Hassan Bhuiyan