by Lee Shing-yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 126-133). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Problem Statement --- p.4 / Chapter 1.2 --- Contributions --- p.4 / Chapter 1.3 --- Thesis Organization --- p.5 / Chapter 2 --- Background --- p.7 / Chapter 2.1 --- Bayesian Networks --- p.7 / Chapter 2.1.1 --- A Simple Example [42] --- p.8 / Chapter 2.1.2 --- Formal Description and Notations --- p.9 / Chapter 2.1.3 --- Learning Bayesian Network from Data --- p.14 / Chapter 2.1.4 --- Inference on Bayesian Networks --- p.18 / Chapter 2.1.5 --- Applications of Bayesian Networks --- p.19 / Chapter 2.2 --- Bayesian Network Classifiers --- p.20 / Chapter 2.2.1 --- The Classification Problem in General --- p.20 / Chapter 2.2.2 --- Bayesian Classifiers --- p.21 / Chapter 2.2.3 --- Bayesian Network Classifiers --- p.22 / Chapter 2.3 --- Evolutionary Computation --- p.28 / Chapter 2.3.1 --- Four Kinds of Evolutionary Computation --- p.29 / Chapter 2.3.2 --- Cooperative Coevolution --- p.31 / Chapter 3 --- Bayesian Network Learning Algorithms --- p.33 / Chapter 3.1 --- Related Work --- p.34 / Chapter 3.1.1 --- Using GA --- p.34 / Chapter 3.1.2 --- Using EP --- p.36 / Chapter 3.1.3 --- Criticism of the Previous Approaches --- p.37 / Chapter 3.2 --- Two New Strategies --- p.38 / Chapter 3.2.1 --- A Hybrid Framework --- p.38 / Chapter 3.2.2 --- A New Operator --- p.39 / Chapter 3.3 --- CCGA --- p.44 / Chapter 3.3.1 --- The Algorithm --- p.45 / Chapter 3.3.2 --- CI Test Phase --- p.46 / Chapter 3.3.3 --- Cooperative Coevolution Search Phase --- p.47 / Chapter 3.4 --- HEP --- p.52 / Chapter 3.4.1 --- A Novel Realization of the Hybrid Framework --- p.54 / Chapter 3.4.2 --- Merging in HEP --- p.55 / Chapter 3.4.3 --- Prevention of Cycle Formation --- p.55 / Chapter 3.5 --- Summary --- p.56 / Chapter 4 --- Evaluation of Proposed Learning Algorithms --- p.57 / Chapter 4.1 --- Experimental Methodology --- p.57 / Chapter 4.2 --- Comparing the Learning Algorithms --- p.61 / Chapter 4.2.1 --- Comparing CCGA with MDLEP --- p.63 / Chapter 4.2.2 --- Comparing HEP with MDLEP --- p.65 / Chapter 4.2.3 --- Comparing CCGA with HEP --- p.68 / Chapter 4.3 --- Performance Analysis of CCGA --- p.70 / Chapter 4.3.1 --- Effect of Different α --- p.70 / Chapter 4.3.2 --- Effect of Different Population Sizes --- p.72 / Chapter 4.3.3 --- Effect of Varying Crossover and Mutation Probabilities --- p.73 / Chapter 4.3.4 --- Effect of Varying Belief Factor --- p.76 / Chapter 4.4 --- Performance Analysis of HEP --- p.77 / Chapter 4.4.1 --- The Hybrid Framework and the Merge Operator --- p.77 / Chapter 4.4.2 --- Effect of Different Population Sizes --- p.80 / Chapter 4.4.3 --- Effect of Different --- p.81 / Chapter 4.4.4 --- Efficiency of the Merge Operator --- p.84 / Chapter 4.5 --- Summary --- p.85 / Chapter 5 --- Learning Bayesian Network Classifiers --- p.87 / Chapter 5.1 --- Issues in Learning Bayesian Network Classifiers --- p.88 / Chapter 5.2 --- The Multinet Classifier --- p.89 / Chapter 5.3 --- The Augmented Bayesian Network Classifier --- p.91 / Chapter 5.4 --- Experimental Methodology --- p.94 / Chapter 5.5 --- Experimental Results --- p.97 / Chapter 5.6 --- Discussion --- p.103 / Chapter 5.7 --- Application in Direct Marketing --- p.106 / Chapter 5.7.1 --- The Direct Marketing Problem --- p.106 / Chapter 5.7.2 --- Response Models --- p.108 / Chapter 5.7.3 --- Experiment --- p.109 / Chapter 5.8 --- Summary --- p.115 / Chapter 6 --- Conclusion --- p.116 / Chapter 6.1 --- Summary --- p.116 / Chapter 6.2 --- Future Work --- p.118 / Chapter A --- A Supplementary Parameter Study --- p.120 / Chapter A.1 --- Study on CCGA --- p.120 / Chapter A.1.1 --- Effect of Different α --- p.120 / Chapter A.1.2 --- Effect of Different Population Sizes --- p.121 / Chapter A.1.3 --- Effect of Varying Crossover and Mutation Probabilities --- p.121 / Chapter A.1.4 --- Effect of Varying Belief Factor --- p.122 / Chapter A.2 --- Study on HEP --- p.123 / Chapter A.2.1 --- The Hybrid Framework and the Merge Operator --- p.123 / Chapter A.2.2 --- Effect of Different Population Sizes --- p.124 / Chapter A.2.3 --- Effect of Different Δα --- p.124 / Chapter A.2.4 --- Efficiency of the Merge Operator --- p.125
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_323479 |
| Date | January 2001 |
| Contributors | Lee, Shing-yan., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, x, 133 leaves : ill. (some col.) ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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