Induction of classification rules and decision trees using genetic algorithms.

Abstract

Ng Sai-Cheong.Thesis submitted in: December 2004.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 172-178).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining --- p.1Chapter 1.2 --- Problem Specifications and Motivations --- p.3Chapter 1.3 --- Contributions of the Thesis --- p.5Chapter 1.4 --- Thesis Roadmap --- p.6Chapter 2 --- Related Work --- p.9Chapter 2.1 --- Supervised Classification Techniques --- p.9Chapter 2.1.1 --- Classification Rules --- p.9Chapter 2.1.2 --- Decision Trees --- p.11Chapter 2.2 --- Evolutionary Algorithms --- p.19Chapter 2.2.1 --- Genetic Algorithms --- p.19Chapter 2.2.2 --- Genetic Programming --- p.24Chapter 2.2.3 --- Evolution Strategies --- p.26Chapter 2.2.4 --- Evolutionary Programming --- p.32Chapter 2.3 --- Applications of Evolutionary Algorithms to Induction of Classification Rules --- p.33Chapter 2.3.1 --- SCION --- p.33Chapter 2.3.2 --- GABIL --- p.34Chapter 2.3.3 --- LOGENPRO --- p.35Chapter 2.4 --- Applications of Evolutionary Algorithms to Construction of Decision Trees --- p.35Chapter 2.4.1 --- Binary Tree Genetic Algorithm --- p.35Chapter 2.4.2 --- OC1-GA --- p.36Chapter 2.4.3 --- OC1-ES --- p.38Chapter 2.4.4 --- GATree --- p.38Chapter 2.4.5 --- Induction of Linear Decision Trees using Strong Typing GP --- p.39Chapter 2.5 --- Spatial Data Structures and its Applications --- p.40Chapter 2.5.1 --- Spatial Data Structures --- p.40Chapter 2.5.2 --- Applications of Spatial Data Structures --- p.42Chapter 3 --- Induction of Classification Rules using Genetic Algorithms --- p.45Chapter 3.1 --- Introduction --- p.45Chapter 3.2 --- Rule Learning using Genetic Algorithms --- p.46Chapter 3.2.1 --- Population Initialization --- p.47Chapter 3.2.2 --- Fitness Evaluation of Chromosomes --- p.49Chapter 3.2.3 --- Token Competition --- p.50Chapter 3.2.4 --- Chromosome Elimination --- p.51Chapter 3.2.5 --- Rule Migration --- p.52Chapter 3.2.6 --- Crossover --- p.53Chapter 3.2.7 --- Mutation --- p.55Chapter 3.2.8 --- Calculating the Number of Correctly Classified Training Samples in a Rule Set --- p.56Chapter 3.3 --- Performance Evaluation --- p.56Chapter 3.3.1 --- Performance Comparison of the GA-based CPRLS and Various Supervised Classifi- cation Algorithms --- p.57Chapter 3.3.2 --- Performance Comparison of the GA-based CPRLS and RS-based CPRLS --- p.68Chapter 3.3.3 --- Effects of Token Competition --- p.69Chapter 3.3.4 --- Effects of Rule Migration --- p.70Chapter 3.4 --- Chapter Summary --- p.73Chapter 4 --- Genetic Algorithm-based Quadratic Decision Trees --- p.74Chapter 4.1 --- Introduction --- p.74Chapter 4.2 --- Construction of Quadratic Decision Trees --- p.76Chapter 4.3 --- Evolving the Optimal Quadratic Hypersurface using Genetic Algorithms --- p.77Chapter 4.3.1 --- Population Initialization --- p.80Chapter 4.3.2 --- Fitness Evaluation --- p.81Chapter 4.3.3 --- Selection --- p.81Chapter 4.3.4 --- Crossover --- p.82Chapter 4.3.5 --- Mutation --- p.83Chapter 4.4 --- Performance Evaluation --- p.84Chapter 4.4.1 --- Performance Comparison of the GA-based QDT and Various Supervised Classification Algorithms --- p.85Chapter 4.4.2 --- Performance Comparison of the GA-based QDT and RS-based QDT --- p.92Chapter 4.4.3 --- Effects of Changing Parameters of the GA-based QDT --- p.93Chapter 4.5 --- Chapter Summary --- p.109Chapter 5 --- Induction of Linear and Quadratic Decision Trees using Spatial Data Structures --- p.111Chapter 5.1 --- Introduction --- p.111Chapter 5.2 --- Construction of k-D Trees --- p.113Chapter 5.3 --- Construction of Generalized Quadtrees --- p.119Chapter 5.4 --- Induction of Oblique Decision Trees using Spatial Data Structures --- p.124Chapter 5.5. --- Induction of Quadratic Decision Trees using Spatial Data Structures --- p.130Chapter 5.6 --- Performance Evaluation --- p.139Chapter 5.6.1 --- Performance Comparison with Various Supervised Classification Algorithms --- p.142Chapter 5.6.2 --- Effects of Changing the Minimum Number of Training Samples at Each Node of a k-D Tree --- p.155Chapter 5.6.3 --- Effects of Changing the Minimum Number of Training Samples at Each Node of a Generalized Quadtree --- p.157Chapter 5.6.4 --- Effects of Changing the Size of Datasets . --- p.158Chapter 5.7 --- Chapter Summary --- p.160Chapter 6 --- Conclusions --- p.164Chapter 6.1 --- Contributions --- p.164Chapter 6.2 --- Future Work --- p.167Chapter A --- Implementation of Data Mining Algorithms Specified in the Thesis --- p.170Bibliography --- p.17