Type of Document Dissertation Author Tang, Yuchun Author's Email Address tyczjs@yahoo.com URN etd-04192006-111652 Title Granular Support Vector Machines Based on Granular Computing, Soft Computing and Statistical Learning Degree Ph.D. Department Computer Science Advisory Committee
Advisor Name Title Yan-Qing Zhang Committee Chair Raj Sunderraman Committee Member Robert Harrison Committee Member Yichuan Zhao Committee Member Keywords
- Statistical Learning
- Computational Intelligence
- Granular Computing
- Bioinformatics
- Granular Support Vector Machines
- Machine Learning
- Data Mining
Date of Defense 2005-12-02 Availability unrestricted Abstract With emergence of biomedical informatics, Web intelligence, and E-business, new challenges are coming for knowledge discovery and data mining modeling problems.In this dissertation work, a framework named Granular Support Vector Machines (GSVM) is proposed to systematically and formally combine statistical learning theory, granular computing theory and soft computing theory to address challenging predictive data modeling problems effectively and/or efficiently, with specific focus on binary classification problems. In general, GSVM works in 3 steps. Step 1 is granulation to build a sequence of information granules from the original dataset or from the original feature space. Step 2 is modeling Support Vector Machines (SVM) in some of these information granules when necessary. Finally, step 3 is aggregation to consolidate information in these granules at suitable abstract level. A good granulation method to find suitable granules is crucial for modeling a good GSVM.
Under this framework, many different granulation algorithms including the GSVM-CMW (cumulative margin width) algorithm, the GSVM-AR (association rule mining) algorithm, a family of GSVM-RFE (recursive feature elimination) algorithms, the GSVM-DC (data cleaning) algorithm and the GSVM-RU (repetitive undersampling) algorithm are designed for binary classification problems with different characteristics.
The empirical studies in biomedical domain and many other application domains demonstrate that the framework is promising.
As a preliminary step, this dissertation work will be extended in the future to build a Granular Computing based Predictive Data Modeling framework (GrC-PDM) with which we can create hybrid adaptive intelligent data mining systems for high quality prediction.
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