Info
Integrated Uncertainty in Knowledge Modelling and Decision Making [[electronic resource] ] : International Symposium, IUKM 2011, Hangzhou, China, October 28-30, 2011, Proceedings / / edited by Yongchuan Tang, Van-Nam Huynh, Jonathan Lawry
| Integrated Uncertainty in Knowledge Modelling and Decision Making [[electronic resource] ] : International Symposium, IUKM 2011, Hangzhou, China, October 28-30, 2011, Proceedings / / edited by Yongchuan Tang, Van-Nam Huynh, Jonathan Lawry |
| Edizione | [1st ed. 2011.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011 |
| Descrizione fisica | 1 online resource (XII, 272 p. 60 illus., 28 illus. in color.) |
| Disciplina | 006.3/32 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Artificial intelligence
Application software Database management Information storage and retrieval Data mining Algorithms Artificial Intelligence Information Systems Applications (incl. Internet) Database Management Information Storage and Retrieval Data Mining and Knowledge Discovery Algorithm Analysis and Problem Complexity |
| ISBN | 3-642-24918-3 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996465418103316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Uncertainty Modeling for Data Mining : A Label Semantics Approach / / by Zengchang Qin, Yongchuan Tang
| Uncertainty Modeling for Data Mining : A Label Semantics Approach / / by Zengchang Qin, Yongchuan Tang |
| Autore | Qin Zengchang |
| Edizione | [1st ed. 2014.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 |
| Descrizione fisica | 1 online resource (303 p.) |
| Disciplina |
004
004.0151 005.7 006.3 006.312 |
| Collana | Advanced Topics in Science and Technology in China |
| 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 |
| ISBN | 3-642-41251-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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 |
| Record Nr. | UNINA-9910298991203321 |
Qin Zengchang
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| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2014 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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