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Fuzzy-Granular Based Data Mining For Effective Decision Support In Biomedical Applications, Yuanchen He
Fuzzy-Granular Based Data Mining For Effective Decision Support In Biomedical Applications, Yuanchen He
Computer Science Dissertations
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 …
Granular Support Vector Machines Based On Granular Computing, Soft Computing And Statistical Learning, Yuchun Tang
Granular Support Vector Machines Based On Granular Computing, Soft Computing And Statistical Learning, Yuchun Tang
Computer Science Dissertations
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 …