Type of Document Dissertation Author He, Yuanchen Author's Email Address heyuanchen78@yahoo.com URN etd-12042006-124044 Title Fuzzy-Granular Based Data Mining for Effective Decision Support in Biomedical Applications Degree Ph.D. Department Computer Science Advisory Committee
Advisor Name Title Dr. Rajshekhar Sunderraman Committee Co-Chair Dr. Yanqing Zhang Committee Co-Chair Dr. Saeid Belkasim Committee Member Dr. Yichuan Zhao Committee Member Keywords
- Granular Computing
- Fuzzy Association Rule Mining
- Decision Support System
- Binary Classification
- Bioinformatics
- Computational Intelligence
- Data Mining
- Knowledge Discovery
Date of Defense 2006-11-15 Availability unrestricted Abstract Due to complexity of biomedical problems, adaptive and intelligent knowledge discovery and data mining systems are highly needed to help humans to understand the inherent mechanism of diseases. For biomedical classification problems, typically it is impossible to build a perfect classifier with 100% prediction accuracy. Hence a more realistic target is to build an effective Decision Support System (DSS).In this dissertation, a novel adaptive Fuzzy Association Rules (FARs) mining algorithm, named FARM-DS, is proposed to build such a DSS for binary classification problems in the biomedical domain. Empirical studies show that FARM-DS is competitive to state-of-the-art classifiers in terms of prediction accuracy. More importantly, FARs can provide strong decision support on disease diagnoses due to their easy interpretability.
This dissertation also proposes a fuzzy-granular method to select informative and discriminative genes from huge microarray gene expression data. With fuzzy granulation, information loss in the process of gene selection is decreased. As a result, more informative genes for cancer classification are selected and more accurate classifiers can be modeled. Empirical studies show that the proposed method is more accurate than traditional algorithms for cancer classification. And hence we expect that genes being selected can be more helpful for further biological studies.
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