Outlier detection based on approximation accuracy entropy
暂无分享,去创建一个
Hongbo Zhao | Feng Jiang | Yanjun Peng | Junwei Du | Yu Xue | Yu Xue | Yanjun Peng | Hongbo Zhao | Junwei Du | Feng Jiang
[1] Cungen Cao,et al. A hybrid approach to outlier detection based on boundary region , 2011, Pattern Recognit. Lett..
[2] Qinghua Hu,et al. Feature selection based on maximal neighborhood discernibility , 2018, Int. J. Mach. Learn. Cybern..
[3] Ke Zhang,et al. A New Local Distance-Based Outlier Detection Approach for Scattered Real-World Data , 2009, PAKDD.
[4] Steven J. Burian,et al. Detection of Urban-Induced Rainfall Anomalies in a Major Coastal City , 2003 .
[5] Graham J. Williams,et al. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms , 2000, KDD '00.
[6] Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .
[7] Feng Jiang,et al. An Approximation Decision Entropy Based Decision Tree Algorithm and Its Application in Intrusion Detection , 2012, RSKT.
[8] Sam Kwong,et al. Incorporating Diversity and Informativeness in Multiple-Instance Active Learning , 2017, IEEE Transactions on Fuzzy Systems.
[9] Jiawei Han,et al. Data Mining: Concepts and Techniques , 2000 .
[10] Bernhard Sick,et al. Novelty detection with CANDIES: a holistic technique based on probabilistic models , 2018, Int. J. Mach. Learn. Cybern..
[11] Theodore Johnson,et al. Fast Computation of 2-Dimensional Depth Contours , 1998, KDD.
[12] Qinghua Hu,et al. A Fitting Model for Feature Selection With Fuzzy Rough Sets , 2017, IEEE Transactions on Fuzzy Systems.
[13] Cungen Cao,et al. An information entropy-based approach to outlier detection in rough sets , 2010, Expert Syst. Appl..
[14] Witold Pedrycz,et al. A Study on Relationship Between Generalization Abilities and Fuzziness of Base Classifiers in Ensemble Learning , 2015, IEEE Transactions on Fuzzy Systems.
[15] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[16] Ramin Mehran,et al. Abnormal crowd behavior detection using social force model , 2009, CVPR.
[17] Nishchal K. Verma,et al. Clustering based outlier detection in fuzzy SVM , 2014, 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[18] Ivo Düntsch,et al. Uncertainty Measures of Rough Set Prediction , 1998, Artif. Intell..
[19] Degang Chen,et al. Attribute Reduction for Heterogeneous Data Based on the Combination of Classical and Fuzzy Rough Set Models , 2014, IEEE Transactions on Fuzzy Systems.
[20] Ran Wang,et al. Noniterative Deep Learning: Incorporating Restricted Boltzmann Machine Into Multilayer Random Weight Neural Networks , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[21] Claude E. Shannon,et al. The mathematical theory of communication , 1950 .
[22] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[23] Vir V. Phoha,et al. K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods , 2007, IEEE Transactions on Knowledge and Data Engineering.
[24] Ashkan Sami,et al. Entropy-based outlier detection using semi-supervised approach with few positive examples , 2014, Pattern Recognit. Lett..
[25] Osmar R. Zaïane,et al. Knowledge and Information Systems Class Separation through Variance : A new Application of Outlier Detection , 2010 .
[26] Lev V. Utkin,et al. A framework for imprecise robust one-class classification models , 2014, Int. J. Mach. Learn. Cybern..
[27] Victoria J. Hodge,et al. A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.
[28] Shengrui Wang,et al. Information-Theoretic Outlier Detection for Large-Scale Categorical Data , 2013, IEEE Transactions on Knowledge and Data Engineering.
[29] Bianca Zadrozny,et al. Outlier detection by active learning , 2006, KDD '06.
[30] Yuhui Zheng,et al. Image segmentation by generalized hierarchical fuzzy C-means algorithm , 2015, J. Intell. Fuzzy Syst..
[31] Jiye Liang,et al. A new measure of uncertainty based on knowledge granulation for rough sets , 2009, Inf. Sci..
[32] Clara Pizzuti,et al. Fast Outlier Detection in High Dimensional Spaces , 2002, PKDD.
[33] Felix Naumann,et al. Data fusion , 2009, CSUR.
[34] Raymond T. Ng,et al. Distance-based outliers: algorithms and applications , 2000, The VLDB Journal.
[35] Shian-Shyong Tseng,et al. Two-phase clustering process for outliers detection , 2001, Pattern Recognit. Lett..
[36] Qinghua Hu,et al. Rank Entropy-Based Decision Trees for Monotonic Classification , 2012, IEEE Transactions on Knowledge and Data Engineering.
[37] Philip S. Yu,et al. Outlier detection for high dimensional data , 2001, SIGMOD '01.
[38] Hans-Peter Kriegel,et al. LoOP: local outlier probabilities , 2009, CIKM.
[39] Ming-Wen Shao,et al. A unified information measure for general binary relations , 2017, Knowl. Based Syst..
[40] Qinghua Hu,et al. Multi-granularity distance metric learning via neighborhood granule margin maximization , 2014, Inf. Sci..
[41] Feng Jiang,et al. Outlier detection based on granular computing and rough set theory , 2014, Applied Intelligence.
[42] Jiye Liang,et al. A New Method for Measuring the Uncertainty in Incomplete Information Systems , 2009, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[43] Bin Gu,et al. A Robust Regularization Path Algorithm for $\nu $ -Support Vector Classification , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[44] Rajeev Rastogi,et al. Efficient algorithms for mining outliers from large data sets , 2000, SIGMOD 2000.
[45] Jerzy W. Grzymala-Busse,et al. Rough Sets , 1995, Commun. ACM.
[46] Ran Wang,et al. Discovering the Relationship Between Generalization and Uncertainty by Incorporating Complexity of Classification , 2018, IEEE Transactions on Cybernetics.
[47] Hans-Peter Kriegel,et al. Angle-based outlier detection in high-dimensional data , 2008, KDD.
[48] Ming-Wen Shao,et al. Feature subset selection based on fuzzy neighborhood rough sets , 2016, Knowl. Based Syst..
[49] Wang Guo,et al. Decision Table Reduction based on Conditional Information Entropy , 2002 .
[50] Fabrizio Angiulli,et al. Exploiting domain knowledge to detect outliers , 2013, Data Mining and Knowledge Discovery.
[51] Jianwu Dang,et al. Multi-kernel SVM based depression recognition using social media data , 2019, Int. J. Mach. Learn. Cybern..
[52] Peter J. Rousseeuw,et al. Robust regression and outlier detection , 1987 .
[53] Hans-Peter Kriegel,et al. Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data , 2009, PAKDD.
[54] Guo Wenzhong,et al. Feature Selection of the Intrusion Detection Data Based on Particle Swarm Optimization and Neighborhood Reduction , 2010 .
[55] Zengyou He,et al. An Optimization Model for Outlier Detection in Categorical Data , 2005, ICIC.
[56] Robert P. W. Duin,et al. Support Vector Data Description , 2004, Machine Learning.
[57] Usama M. Fayyad,et al. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.
[58] Xu Zhang,et al. A Quick Attribute Reduction Algorithm with Complexity of max(O(|C||U|),O(|C|~2|U/C|)) , 2006 .
[59] Qinghua Hu,et al. Feature Selection Based on Neighborhood Discrimination Index , 2018, IEEE Transactions on Neural Networks and Learning Systems.
[60] David J. Hand,et al. Statistical fraud detection: A review , 2002 .
[61] Eleazar Eskin,et al. A GEOMETRIC FRAMEWORK FOR UNSUPERVISED ANOMALY DETECTION: DETECTING INTRUSIONS IN UNLABELED DATA , 2002 .
[62] VARUN CHANDOLA,et al. Anomaly detection: A survey , 2009, CSUR.
[63] Vic Barnett,et al. Outliers in Statistical Data , 1980 .
[64] Anthony K. H. Tung,et al. Ranking Outliers Using Symmetric Neighborhood Relationship , 2006, PAKDD.
[65] Hans-Peter Kriegel,et al. LOF: identifying density-based local outliers , 2000, SIGMOD 2000.
[66] Jong-Seok Lee,et al. A precise ranking method for outlier detection , 2015, Inf. Sci..
[67] Charu C. Aggarwal,et al. Outlier Analysis , 2013, Springer New York.
[68] Feng Jiang,et al. Initialization of K-modes clustering using outlier detection techniques , 2016, Inf. Sci..
[69] Jiye Liang,et al. Information entropy, rough entropy and knowledge granulation in incomplete information systems , 2006, Int. J. Gen. Syst..
[70] Jiye Liang,et al. The Information Entropy, Rough Entropy And Knowledge Granulation In Rough Set Theory , 2004, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[71] Miao Duo,et al. A HEURISTIC ALGORITHM FOR REDUCTION OF KNOWLEDGE , 1999 .
[72] Witold Pedrycz,et al. Selecting Discrete and Continuous Features Based on Neighborhood Decision Error Minimization , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).