Ting, Kai Ming

41 publications

MLJ 2025 A New Filter for Deformation-Invariant Persistence Diagram Kaifeng Zhang, Hang Zhang, Kai Ming Ting, Tianrun Liang

ECML-PKDD 2025 Machine Unlearning for Random Forest via Method of Images Hang Zhang, Kai Ming Ting

JAIR 2025 Towards a Robust Persistence Diagram via Data-Dependent Kernel Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu

ECML-PKDD 2025 Voronoi Diagram Encoded Hashing Yang Xu, Kai Ming Ting

JAIR 2024 A Principled Distributional Approach to Trajectory Similarity Measurement and Its Application to Anomaly Detection Yufan Wang, Zijing Wang, Kai Ming Ting, Yuanyi Shang

JAIR 2024 Detecting Change Intervals with Isolation Distributional Kernel Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li

IJCAI 2024 Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint) Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li

ICML 2023 Towards a Persistence Diagram That Is Robust to Noise and Varied Densities Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu

IJCAI 2022 Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract) Ye Zhu, Kai Ming Ting

JAIR 2021 Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel Ye Zhu, Kai Ming Ting

AAAI 2021 Isolation Graph Kernel Bi-Cun Xu, Kai Ming Ting, Yuan Jiang

MLJ 2019 Lowest Probability Mass Neighbour Algorithms: Relaxing the Metric Constraint in Distance-Based Neighbourhood Algorithms Kai Ming Ting, Ye Zhu, Mark J. Carman, Yue Zhu, Takashi Washio, Zhi-Hua Zhou

AAAI 2019 Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent C. S. Lee

MLJ 2018 Local Contrast as an Effective Means to Robust Clustering Against Varying Densities Bo Chen, Kai Ming Ting, Takashi Washio, Ye Zhu

MLJ 2017 Defying the Gravity of Learning Curve: A Characteristic of Nearest Neighbour Anomaly Detectors Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Sunil Aryal

AAAI 2017 Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou

MLJ 2016 Commentary: A Decomposition of the Outlier Detection Problem into a Set of Supervised Learning Problems Ye Zhu, Kai Ming Ting

JAIR 2016 ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets Guansong Pang, Kai Ming Ting, David W. Albrecht, Huidong Jin

MLJ 2015 Half-Space Mass: A Maximally Robust and Efficient Data Depth Method Bo Chen, Kai Ming Ting, Takashi Washio, Gholamreza Haffari

MLJ 2013 Mass Estimation Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, Swee Chuan Tan

IJCAI 2013 Optimizing Cepstral Features for Audio Classification Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang

MLJ 2012 Learning by Extrapolation from Marginal to Full-Multivariate Probability Distributions: Decreasingly Naive Bayesian Classification Geoffrey I. Webb, Janice R. Boughton, Fei Zheng, Kai Ming Ting, Houssam Salem

ECML-PKDD 2011 Building Sparse Support Vector Machines for Multi-Instance Classification Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang

IJCAI 2011 Fast Anomaly Detection for Streaming Data Swee Chuan Tan, Kai Ming Ting, Fei Tony Liu

MLJ 2011 Feature-Subspace Aggregating: Ensembles for Stable and Unstable Learners Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb

ECML-PKDD 2010 On Detecting Clustered Anomalies Using SCiForest Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou

JAIR 2008 Spectrum of Variable-Random Trees Fei Tony Liu, Kai Ming Ting, Yang Yu, Zhi-Hua Zhou

MLJ 2007 Classifying Under Computational Resource Constraints: Anytime Classification Using Probabilistic Estimators Ying Yang, Geoffrey I. Webb, Kevin B. Korb, Kai Ming Ting

ECML-PKDD 2006 To Select or to Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles Ying Yang, Geoffrey I. Webb, Jesús Cerquides, Kevin B. Korb, Janice R. Boughton, Kai Ming Ting

MLJ 2005 On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions Geoffrey I. Webb, Kai Ming Ting

ECML-PKDD 2004 Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees Kai Ming Ting

ICML 2002 Issues in Classifier Evaluation Using Optimal Cost Curves Kai Ming Ting

ICML 2000 A Comparative Study of Cost-Sensitive Boosting Algorithms Kai Ming Ting

ECML-PKDD 2000 An Empirical Study of MetaCost Using Boosting Algorithms Kai Ming Ting

JAIR 1999 Issues in Stacked Generalization Kai Ming Ting, Ian H. Witten

ICML 1999 Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting

ECML-PKDD 1998 Boosting Trees for Cost-Sensitive Classifications Kai Ming Ting, Zijian Zheng

ECML-PKDD 1997 Model Combination in the Multiple-Data-Batches Scenario Kai Ming Ting, Boon Toh Low

IJCAI 1997 Stacked Generalizations: When Does It Work? Kai Ming Ting, Ian H. Witten

ICML 1997 Stacking Bagged and Dagged Models Kai Ming Ting, Ian H. Witten

ICML 1996 The Characterisation of Predictive Accuracy and Decision Combination Kai Ming Ting