Title page for etd-0909111-070933


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URN etd-0909111-070933
Author Feng-Shih Lin
Author's Email Address No Public.
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Department Computer Science and Engineering
Year 2010
Semester 2
Degree Master
Type of Document
Language English
Title Improved Approaches for Attribute Clustering Based on the Group Genetic Algorithm
Date of Defense 2011-07-22
Page Count 78
Keyword
  • feature selection
  • genetic algorithm
  • grouping genetic algorithm
  • data mining
  • Attribute clustering
  • Abstract Feature selection is a pre-processing step in data-mining and machine learning, and plays an important role for analyzing high-dimensional data. Appropriately selected features can not only reduce the complexity of the mining or learning process, but also improve the accuracy of results. In the past, the concept of performing the task of feature selection by attribute clustering was proposed. If similar attributes could be clustered into groups, attributes could be easily replaced by others in the same group when some attribute values were missed. Hong et al. also proposed several genetic algorithms for finding appropriate attribute clusters. Their approaches, however, suffered from the weakness that multiple chromosomes would represent the same attribute clustering result (feasible solution) due to the combinatorial property, thus causing a larger search space than needed. In this thesis, we thus attempt to improve the performance of the GA-based attribute-clustering process based on the grouping genetic algorithm (GGA). Two GGA-based attribute clustering approaches are proposed. In the first approach, the general GGA representation and operators are used to reduce the redundancy of chromosome representation for attribute clustering. In the second approach, a new encoding scheme with corresponding crossover and mutation operators are designed, and an improved fitness function is proposed to achieve better convergence speed and provide more flexible alternatives than the first one. At last, experiments are made to compare the efficiency and the accuracy of the proposed approaches and the previous ones.
    Advisory Committee
  • Chung-Nan Lee - chair
  • Cha-Hwa Lin - co-chair
  • Shyue-Liang Wang - co-chair
  • Tzung-Pei Hong - advisor
  • Files
  • etd-0909111-070933.pdf
  • Indicate in-campus at 3 year and off-campus access at 3 year.
    Date of Submission 2011-09-09

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