![Outlier ensembles [electronic resource] : an introduction](https://image.aladin.co.kr/product/10231/38/cover/331954764x_2.jpg)
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| 008 | 200107s2017 sz a ob 001 0 eng d | |
| 020 | ▼a 9783319547640 | |
| 020 | ▼a 9783319547657 (e-book) | |
| 040 | ▼a 211009 ▼c 211009 ▼d 211009 | |
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| 084 | ▼a 005.1 ▼2 DDCK | |
| 090 | ▼a 005.1 | |
| 100 | 1 | ▼a Aggarwal, Charu C. |
| 245 | 1 0 | ▼a Outlier ensembles ▼h [electronic resource] : ▼b an introduction / ▼c Charu C. Aggarwal, Saket Sathe. |
| 260 | ▼a Cham : ▼b Springer, ▼c c2017. | |
| 300 | ▼a 1 online resource (xvi, 276 p.) : ▼b ill. (some col.). | |
| 500 | ▼a Title from e-Book title page. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 505 | 0 | ▼a An Introduction to Outlier Ensembles -- Theory of Outlier Ensembles -- Variance Reduction in Outlier Ensembles -- Bias Reduction in Outlier Ensembles: The Guessing Game -- Model Combination Methods for Outlier Ensembles -- Which Outlier Detection Algorithm Should I Use? |
| 520 | ▼a This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design. | |
| 530 | ▼a Issued also as a book. | |
| 538 | ▼a Mode of access: World Wide Web. | |
| 650 | 0 | ▼a Computer algorithms. |
| 650 | 0 | ▼a Data mining. |
| 700 | 1 | ▼a Sathe, Saket. |
| 856 | 4 0 | ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-3-319-54765-7 |
| 945 | ▼a KLPA | |
| 991 | ▼a E-Book(소장) |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
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| No. 1 | 소장처 중앙도서관/e-Book 컬렉션/ | 청구기호 CR 005.1 | 등록번호 E14018509 | 도서상태 대출불가(열람가능) | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem.
This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.
정보제공 :
목차
CONTENTS 1 An Introduction to Outlier Ensembles = 1 1.1 Introduction = 1 1.1.1 Motivations for Ensemble Methods in Outlier Analysis = 3 1.1.2 Common Settings for Existing Ensemble Methods = 4 1.1.3 Types of Ensemble Methods = 6 1.1.4 Overview of Outlier Ensemble Design = 7 1.2 Categorization by Component Independence = 8 1.2.1 Sequential Ensembles = 9 1.2.2 Independent Ensembles = 11 1.3 Categorization by Constituent Components = 12 1.3.1 Model-Centered Ensembles = 12 1.3.2 Data-Centered Ensembles = 14 1.3.3 Discussion of Categorization Schemes = 16 1.4 Categorization by Theoretical Approach = 17 1.4.1 Variance Reduction in Outlier Ensembles = 18 1.4.2 Bias Reduction in Outlier Ensembles = 18 1.5 Defining Combination Functions = 19 1.5.1 Normalization Issues = 19 1.5.2 Combining Scores from Different Models = 20 1.6 Research Overview and Book Organization = 23 1.6.1 Overview of Book = 27 1.7 Conclusions and Discussion = 30 References = 31 2 Theory of Outlier Ensembles = 35 2.1 Introduction = 35 2.2 The Bias-Variance Trade-Off for Outlier Detection = 37 2.2.1 Relationship of Ensemble Analysis to Bias-Variance Trade-Off = 42 2.2.2 Out-of-Sample Issues = 43 2.2.3 Understanding How Ensemble Analysis Works = 44 2.2.4 Data-Centric View Versus Model-Centric View = 50 2.3 Examples and Applications of the Bias-Variance Tradeoff = 58 2.3.1 Bagging and Subsampling = 59 2.3.2 Feature Bagging = 60 2.3.3 Boosting = 61 2.4 Experimental Illustration of Bias-Variance Theory = 61 2.4.1 Understanding the Effects of Ensembles on Data-Centric Bias and Variance = 62 2.4.2 Experimental Examples of Bias-Variance Decomposition = 68 2.5 Conclusions = 72 References = 73 3 Variance Reduction in Outlier Ensembles = 75 3.1 Introduction = 75 3.2 Motivations for Basic Variance Reduction Framework = 78 3.3 Variance Reduction Is Not a Panacea = 83 3.3.1 When Does Data-Centric Variance Reduction Help? = 84 3.3.2 When Does Model-Centric Variance Reduction Help? = 91 3.3.3 The Subtle Differences Between AUCs and MSEs = 93 3.4 Variance Reduction Methods = 93 3.4.1 Feature Bagging (FB) for High-Dimensional Outlier Detection = 94 3.4.2 Rotated Bagging (RB) = 99 3.4.3 Projected Clustering and Subspace Histograms = 100 3.4.4 The Point-Wise Bagging and Subsampling Class of Methods = 107 3.4.5 Wagging (WAG) = 130 3.4.6 Data-Centric and Model-Centric Perturbation = 131 3.4.7 Parameter-Centric Ensembles = 131 3.4.8 Explicit Randomization of Base Models = 132 3.5 Some New Techniques for Variance Reduction = 134 3.5.1 Geometric Subsampling (GS) = 134 3.5.2 Randomized Feature Weighting (RFW) = 136 3.6 Forcing Stability by Reducing Impact of Abnormal Detector Executions = 137 3.6.1 Performance Analysis of Trimmed Combination Methods = 140 3.6.2 Discussion of Commonly Used Combination Methods = 143 3.7 Performance Analysis of Methods = 145 3.7.1 Data Set Descriptions = 145 3.7.2 Comparison of Variance Reduction Methods = 147 3.8 Conclusions = 157 References = 158 4 Bias Reduction in Outlier Ensembles : The Guessing Game = 163 4.1 Introduction = 163 4.2 Bias Reduction in Classification and Outlier Detection = 165 4.2.1 Boosting = 166 4.2.2 Training Data Pruning = 167 4.2.3 Model Pruning = 168 4.2.4 Model Weighting = 169 4.2.5 Differences Between Classification and Outlier Detection = 170 4.3 Training Data Pruning = 171 4.3.1 Deterministic Pruning = 171 4.3.2 Fixed Bias Sampling = 172 4.3.3 Variable Bias Sampling = 174 4.4 Model Pruning = 175 4.4.1 Implicit Model Pruning in Subspace Outlier Detection = 178 4.4.2 Revisiting Pruning by Trimming = 178 4.4.3 Model Weighting = 180 4.5 Supervised Bias Reduction with Unsupervised Feature Engineering = 181 4.6 Bias Reduction by Human Intervention = 182 4.7 Conclusions = 184 References = 184 5 Model Combination Methods for Outlier Ensembles = 187 5.1 Introduction = 187 5.2 Impact of Outlier Evaluation Measures = 190 5.3 Score Normalization Issues = 193 5.4 Model Combination for Variance Reduction = 195 5.5 Model Combination for Bias Reduction = 196 5.5.1 A Simple Example = 198 5.5.2 Sequential Combination Methods = 199 5.6 Combining Bias and Variance Reduction = 200 5.6.1 Factorized Consensus = 201 5.7 Using Mild Supervision in Model Combination = 203 5.8 Conclusions and Summary = 204 References = 204 6 Which Outlier Detection Algorithm Should I Use? = 207 6.1 Introduction = 207 6.2 A Review of Classical Distance-Based Detectors = 212 6.2.1 Exact k-Nearest Neighbor Detector = 213 6.2.2 Average k-Nearest Neighbor Detector = 214 6.2.3 An Analysis of Bagged and Subsampled 1-Nearest Neighbor Detectors = 214 6.2.4 Harmonic k-Nearest Neighbor Detector = 216 6.2.5 Local Outlier Factor (LOF) = 217 6.3 A Review of Clustering, Histograms, and Density-Based Methods = 219 6.3.1 Histogram and Clustering Methods = 219 6.3.2 Kernel Density Methods = 224 6.4 A Review of Dependency-Oriented Detectors = 225 6.4.1 Soft PCA : The Mahalanobis Method = 226 6.4.2 Kernel Mahalanobis Method = 231 6.4.3 Decomposing Unsupervised Learning into Supervised Learning Problems = 239 6.4.4 High-Dimensional Outliers Based on Group-Wise Dependencies = 242 6.5 The Hidden Wildcard of Algorithm Parameters = 243 6.5.1 Variable Subsampling and the Tyranny of Parameter Choice = 245 6.6 TRINITY : A Blend of Heterogeneous Base Detectors = 247 6.7 Analysis of Performance = 248 6.7.1 Data Set Descriptions = 249 6.7.2 Specific Details of Setting = 251 6.7.3 Summary of Findings = 253 6.7.4 The Great Equalizing Power of Ensembles = 260 6.7.5 The Argument for a Heterogeneous Combination = 264 6.7.6 Discussion = 269 6.8 Conclusions = 271 References = 271 Index = 275