Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103205
Title: A new unsupervised pre-processing algorithm based on artificial immune system for ERP assessment in a P300-based GKT
Authors: Shojaeilangari, Seyedehsamaneh
Moradi, Mohammad Hassan
Keywords: DRNTU::Science
Issue Date: 2012
Source: Shojaeilangari, S., & Moradi, M. H. (2012). A New Unsupervised pre-processing algorithm based on artificial immune system for ERP assessment in a P300-based GKT. Research journal of applied sciences, engineering and technology, 4(18), 3238-3245.
Series/Report no.: Research journal of applied sciences, engineering and technology
Abstract: In recent years, an increasing number of researches have been focused on bio-inspired algorithms to solve the elaborate engineering problems. Artificial Immune System (AIS) is an artificial intelligence technique which has potential of solving problems in various fields. The immune system, due to self-regulating nature, has been an inspiration source of unsupervised learning methods for pattern recognition task. The purpose of this study is to apply the AIS to pre-process the lie-detection dataset to promote the recognition of guilty and innocent subjects. A new Unsupervised AIS (UAIS) was proposed in this study as a pre-processing method before classification. Then, we applied three different classifiers on pre-processed data for Event Related Potential (ERP) assessment in a P300-based Guilty Knowledge Test (GKT). Experiment results showed that UAIS is a successful pre-processing method which is able to improve the classification rate. In our experiments, we observed that the classification accuracies for three different classifiers: K-Nearest Neighbourhood (KNN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) were increased after applying UAIS pre-processing. Using of scattering criterion to assessment the features before and after pre-processing proved that our proposed method was able to perform data mapping from a primary feature space to a new area where the data separability was improved significantly.
URI: https://hdl.handle.net/10356/103205
http://hdl.handle.net/10220/25770
URL: http://maxwellsci.com/jp/abstract.php?jid=RJASET&no=214&abs=07
ISSN: 2040-7467
Rights: © 2012 Maxwell Scientific Organization. This paper was published in Research Journal of Applied Sciences, Engineering and Technology and is made available as an electronic reprint (preprint) with permission of Maxwell Scientific Organization. The paper can be found at the following official URL: [http://maxwellsci.com/jp/abstract.php?jid=RJASET&no=214&abs=07]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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