LEADER 06468nam 22007935 450 001 9910298991203321 005 20230810211252.0 010 $a3-642-41251-3 024 7 $a10.1007/978-3-642-41251-6 035 $a(CKB)3710000000269735 035 $a(EBL)1968216 035 $a(SSID)ssj0001372381 035 $a(PQKBManifestationID)11831418 035 $a(PQKBTitleCode)TC0001372381 035 $a(PQKBWorkID)11304215 035 $a(PQKB)11165435 035 $a(MiAaPQ)EBC1968216 035 $a(DE-He213)978-3-642-41251-6 035 $a(PPN)182100049 035 $a(EXLCZ)993710000000269735 100 $a20141030d2014 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aUncertainty Modeling for Data Mining $eA Label Semantics Approach /$fby Zengchang Qin, Yongchuan Tang 205 $a1st ed. 2014. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2014. 215 $a1 online resource (303 p.) 225 1 $aAdvanced Topics in Science and Technology in China,$x1995-6827 300 $aDescription based upon print version of record. 311 0 $a3-642-41250-5 320 $aIncludes bibliographical references. 327 $aCover; 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 327 $a2.4 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 327 $a3.4.1 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 327 $a4.5 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 327 $a5.3 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 327 $a6.5 Bayesian Estimation Trees 330 $aMachine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.   Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China. 410 0$aAdvanced Topics in Science and Technology in China,$x1995-6827 606 $aData mining 606 $aArtificial intelligence 606 $aComputer networks 606 $aComputer science$xMathematics 606 $aData Mining and Knowledge Discovery 606 $aArtificial Intelligence 606 $aComputer Communication Networks 606 $aMathematical Applications in Computer Science 615 0$aData mining. 615 0$aArtificial intelligence. 615 0$aComputer networks. 615 0$aComputer science$xMathematics. 615 14$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 615 24$aComputer Communication Networks. 615 24$aMathematical Applications in Computer Science. 676 $a004 676 $a004.0151 676 $a005.7 676 $a006.3 676 $a006.312 700 $aQin$b Zengchang$4aut$4http://id.loc.gov/vocabulary/relators/aut$0875231 702 $aTang$b Yongchuan$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910298991203321 996 $aUncertainty Modeling for Data Mining$91954001 997 $aUNINA