Uncertainty modeling for data mining: a label semantics approach
Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorith…
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- Full title :Uncertainty modeling for data mining : a label semantics approach / Zengchang Qin, Yongchuan Tang.
Summary
Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning. Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.
Read More ↓Edition Statements
- Publication Date:2014.
Publication Statements
- Publisher(s):Springer
- Place of Publication:Dordrecht
- Language:eng
- Publication Date:2014.
The Physical Object
- Description:xviii, 291 pages : : illustrations ; : 24 cm.
Edition Identifiers
- ISBN:9783642412509, 3642412505
- oclc:on1237710461
- Dewey Decimal:003/.54
Table of Contents
Introduction -- Induction and learning -- Label semantics theory -- Linguistic decision trees for classification -- Linguistic decision trees for prediction -- Bayesian methods based on label semantics -- Unsupervised learning with label semantics -- Linguistic FOIL and multiple attribute hierarchy for decision-making -- A prototype theory interpretation of label semantics -- Prototype theory for learning -- Prototype-based rule systems -- Information cells and information cell mixture models.
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