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Document Type:Latin Dissertation
Language of Document:English
Record Number:54017
Doc. No:TL23971
Call number:‭1533961‬
Main Entry:Zeehasham Rasheed
Title & Author:Adaptive Fuzzy Logic based framework for handling imprecision and uncertainty in pattern classification of bioinformatics datasetsZeehasham Rasheed
College:King Fahd University of Petroleum and Minerals (Saudi Arabia)
Date:2009
Degree:M.S.
student score:2009
Page No:109
Abstract:Classification in the emerging field of Bioinformatics is a challenging task because the information about different diseases is either insufficient or lacking in authenticity as data is collected from different types of medical equipment. Also the limitation of human expertise in manual diagnoses leads to incorrect diagnoses. Moreover, the information gathered from various sources is subject to imprecision and uncertainty. Researchers utilized Artificial Neural Networks, Support Vector Machine and Bayesian Networks to achieve better classification, but the developed models are bedeviled by several limitations especially in uncertain situations. Recently, Type-1 and Type-2 Fuzzy Logic Systems (FLS) have been introduced as novel computational intelligence approaches for both prediction and classification. However Type-2 and other FLS have not been fully utilized in the bioinformatics and medical science. This thesis presents a Type-2 FLS-based classification framework for multivariate data to diagnose different types of diseases, which is capable of handling imprecision and uncertainty. As expected, this new computational intelligence approach overcomes the weaknesses of existing classifiers, particularly in the ability to handle data in uncertain situations such as uncertainty due to the existence of various types of noise, inconsistent expert opinions, ignorance and laziness. The classification accuracy and performance of the proposed framework are measured by using University of California, Irvine (UCI) well known medical datasets. The classification is performed on the basis of the nature of the inputs (e.g., singleton or non-singleton) and on whether uncertainty is present or absent. Empirical results have shown that the proposed FLS classification framework outperforms earlier implemented models with better classification accuracy among all existing classifiers. In addition, we conducted empirical studies on this classifier regarding the impact of various parameters of the proposed framework such as training algorithms and defuzzification methods.
Subject:Applied sciences; Computer science; 0984:Computer science
Added Entry:T. H. A.-M. El-Bassuny, Mohamed
Added Entry:King Fahd University of Petroleum and Minerals (Saudi Arabia)