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|Title: ||Probabilistic learning based decision making under uncertainty with application to biomedical process|
|Other Titles: ||Bu que ding huan jing xia ji yu gai lü xue xi de jue ce xi tong ji qi zai sheng wu yi xue guo cheng zhong de ying yong|
|Authors: ||Yang, Jinglin (楊荊林)|
|Department: ||Department of Manufacturing Engineering and Engineering Management|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2010|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Biomedical engineering -- Decision making.|
Uncertainty (Information theory)
|Notes: ||CityU Call Number: R859.7.D42 Y36 2010|
xiv, 145 leaves : ill. 30 cm.
Thesis (Ph.D.)--City University of Hong Kong, 2010.
Includes bibliographical references (leaves 125-144)
|Abstract: ||With the development of technology, humans are getting more and more understanding
about biomedical process. Decision making in biomedical process becomes a hot topic.
One serious challenge in biomedical decision making is the treatment of various
uncertainties. At present, the mechanisms remain unknown for many critical diseases and
the dynamics of cells, proteins and genes, which result in the structural uncertainty in the
modeling of decision making. The difference of samples and the measurement noises will
lead to the stochastic uncertainties in experimental data. When uncertainties are contained
in high dimensional and limited data samples, which are the common characteristics of
biomedical process, the decision making becomes especially difficult.
In this thesis, probabilistic learning methods are proposed for decision making under
uncertainties of biomedical process. The objectives of this thesis include
1) to develop a novel probabilistic support vector machine to reduce the effect of
2) to design a knowledge based probabilistic SVM learning system for pain
3) to propose a new entropy based learning method for feature selection under
4) to design an entropy based sequential learning methodology for gene selection.
The significance and contributions of this thesis will be highlighted as follows.
● A probabilistic support vector machine (PSVM) is proposed for the classification of
data with stochastic uncertainties. In most decision making, the data samples usually
have large dimensions, thus conventional noise reduction methods are less effective.
In PSVM, the uncertainties are modelled as the probability density function (PDF) of
the separating margin in feature space. A principal component analysis (PCA) based distributed SVM system will be used to estimate the probabilistic distribution
function of the separating margin. A new probabilistic optimization is proposed to
determine the decision function. Furthermore, the confidence of the decision is
estimated under uncertain circumstance and its correlation with sample uncertainties
● A novel knowledge based probabilistic support vector learning system is proposed to
deal with the diagnosis and treatment evaluation of LBP. The decision system
consists of qualitative knowledge model and quantitative model. Expert knowledge
and clinical experience are integrated into the design. To deal with the uncertainties
in patients samples, PSVM is employed to learn the decision rules from data.
● A new entropy based feature selection method is propsed to selected informative
features under uncertainties. The samples with different class labels are considered as
the measurement from different systems. Due to the uncertainties and intrinsic
dynamics, the different systems have different entropy. An importance sampling
method is proposed to estimate the probability density function efficiently.
● An entropy based sequential learning methodology is proposed for gene selection.
The spatial entropy is first defined to measure the uncertainties in genes. Genes are
projected into the orthogonal space spanned by the orthogonal basis obtained by
Proper Orthogonal Decomposition (POD). The spatial entropy is calculated by the
approximation of variations on each orthogonal basis. To derive a low-dimensional
classification model, the genes are projected into the feature space that will remove
lots of gene redundancy. Then a neural network (NN) is employed to learn the
parameter of the classification model. The proposed methodology is processed in a
sequential manner until the classification performance is stable and satisfactory.
This thesis proposed two probabilistic learning methods and two decision support
systems for biomecial process. The methods are data driven and could explore the
probability density function of features and integrate the PDF into the design of the decision making. In their applications to biomecial process, the expert knowledge and
experience are extracted for the design, the probabilistic learning reduce the influence of
various uncertainties. The methods have been applied to artificial data sets and many real
life datasets and demonstrated the effectiveness.|
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b3947905|
|Appears in Collections:||MEEM - Doctor of Philosophy |
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