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Uncertainty Modeling for Data Mining : A Label Semantics Approach / / by Zengchang Qin, Yongchuan Tang



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Autore: Qin Zengchang Visualizza persona
Titolo: Uncertainty Modeling for Data Mining : A Label Semantics Approach / / by Zengchang Qin, Yongchuan Tang Visualizza cluster
Pubblicazione: Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014
Edizione: 1st ed. 2014.
Descrizione fisica: 1 online resource (303 p.)
Disciplina: 004
004.0151
005.7
006.3
006.312
Soggetto topico: Data mining
Artificial intelligence
Computer networks
Computer science - Mathematics
Data Mining and Knowledge Discovery
Artificial Intelligence
Computer Communication Networks
Mathematical Applications in Computer Science
Persona (resp. second.): TangYongchuan
Note generali: Description based upon print version of record.
Nota di bibliografia: Includes bibliographical references.
Nota di contenuto: Cover; Title Page; Copyright Page; Dedication Page; Preface; Acknowledgements; Table of Contents; Acronyms; Notations; 1 Introduction; 1.1 Types of Uncertainty; 1.2 Uncertainty Modeling and Data Mining; 1.3 RelatedWorks; References; 2 Induction and Learning; 2.1 Introduction; 2.2 Machine Learning; 2.2.1 Searching in Hypothesis Space; 2.2.2 Supervised Learning; 2.2.3 Unsupervised Learning; 2.2.4 Instance-Based Learning; 2.3 Data Mining and Algorithms; 2.3.1 Why Do We Need Data Mining?; 2.3.2 How Do We do Data Mining?; 2.3.3 Artificial Neural Networks; 2.3.4 Support Vector Machines
2.4 Measurement of Classifiers2.4.1 ROC Analysis for Classification; 2.4.2 Area Under the ROC Curve; 2.5 Summary; References; 3 Label Semantics Theory; 3.1 Uncertainty Modeling with Labels; 3.1.1 Fuzzy Logic; 3.1.2 Computing with Words; 3.1.3 Mass Assignment Theory; 3.2 Label Semantics; 3.2.1 Epistemic View of Label Semantics; 3.2.2 Random Set Framework; 3.2.3 Appropriateness Degrees; 3.2.4 Assumptions for Data Analysis; 3.2.5 Linguistic Translation; 3.3 Fuzzy Discretization; 3.3.1 Percentile-Based Discretization; 3.3.2 Entropy-Based Discretization; 3.4 Reasoning with Fuzzy Labels
3.4.1 Conditional Distribution Given Mass Assignments3.4.2 Logical Expressions of Fuzzy Labels; 3.4.3 Linguistic Interpretation of Appropriate Labels; 3.4.4 Evidence Theory and Mass Assignment; 3.5 Label Relations; 3.6 Summary; References; 4 Linguistic Decision Trees for Classification; 4.1 Introduction; 4.2 Tree Induction; 4.2.1 Entropy; 4.2.2 Soft Decision Trees; 4.3 Linguistic Decision for Classification; 4.3.1 Branch Probability; 4.3.2 Classification by LDT; 4.3.3 Linguistic ID3 Algorithm; 4.4 Experimental Studies; 4.4.1 Influence of the Threshold; 4.4.2 Overlapping Between Fuzzy Labels
4.5 Comparison Studies4.6 Merging of Branches; 4.6.1 Forward Merging Algorithm; 4.6.2 Dual-Branch LDTs; 4.6.3 Experimental Studies for Forward Merging; 4.6.4 ROC Analysis for Forward Merging; 4.7 Linguistic Reasoning; 4.7.1 Linguistic Interpretation of an LDT; 4.7.2 Linguistic Constraints; 4.7.3 Classification of Fuzzy Data; 4.8 Summary; References; 5 Linguistic Decision Trees for Prediction; 5.1 Prediction Trees; 5.2 Linguistic Prediction Trees; 5.2.1 Branch Evaluation; 5.2.2 Defuzzification; 5.2.3 Linguistic ID3 Algorithm for Prediction; 5.2.4 Forward Branch Merging for Prediction
5.3 Experimental Studies5.3.1 3D Surface Regression; 5.3.2 Abalone and Boston Housing Problem; 5.3.3 Prediction of Sunspots; 5.3.4 Flood Forecasting; 5.4 Query Evaluation; 5.4.1 Single Queries; 5.4.2 Compound Queries; 5.5 ROC Analysis for Prediction; 5.5.1 Predictors and Probabilistic Classifiers; 5.5.2 AUC Value for Prediction; 5.6 Summary; References; 6 Bayesian Methods Based on Label Semantics; 6.1 Introduction; 6.2 Naive Bayes; 6.2.1 Bayes Theorem; 6.2.2 Fuzzy Naive Bayes; 6.3 Fuzzy Semi-Naive Bayes; 6.4 Online Fuzzy Bayesian Prediction; 6.4.1 Bayesian Methods; 6.4.2 Online Learning
6.5 Bayesian Estimation Trees
Sommario/riassunto: 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.
Titolo autorizzato: Uncertainty Modeling for Data Mining  Visualizza cluster
ISBN: 3-642-41251-3
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910298991203321
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Serie: Advanced Topics in Science and Technology in China, . 1995-6827