K. Ting

发表

Process-Based Modeling

Stan Matwin, Eyke Hüllermeier, Johannes Fürnkranz, 2010, Encyclopedia of Machine Learning.

Stacked generalization: when does it work?

Ian H. Witten, Kai Ming Ting, I. Witten, 1997, IJCAI 1997.

Data-dependent dissimilarity measure: an effective alternative to geometric distance measures

Gholamreza Haffari, Kai Ming Ting, Takashi Washio, 2017, Knowledge and Information Systems.

Evolutionary Computing

Subbarao Kambhampati, Krzysztof Krawiec, Moshe Sipper, 1997, Lecture Notes in Computer Science.

Samuel's Checkers Player

Risto Miikkulainen, Scott Sanner, Kristian Kersting, 2010, Encyclopedia of Machine Learning.

Spike-Timing-Dependent Plasticity

Risto Miikkulainen, Scott Sanner, Kristian Kersting, 2010, Encyclopedia of Machine Learning.

Discretization of Continuous-Valued Attributes and Instance-Based Learning

Kai Ming Ting, K. Ting, 1994 .

Data Dependent Dissimilarity Measures

A. Kabán, K. Ting, T. Washio, 2018 .

Learning Sparse Kernel Classifiers for Multi-Instance Classification

Zhouyu Fu, Guojun Lu, Kai Ming Ting, 2013, IEEE Transactions on Neural Networks and Learning Systems.

Title : Adaptive Anomaly Detection using Isolation Forest

K. Ting, 2010 .

Second Generation of Mass Estimation

K. Ting, 2013 .

An Empirical Study of MetaCost Using Boosting Algorithms

Kai Ming Ting, K. Ting, 2000, ECML.

Click-Through Rate (CTR)

Jean-Michel Renders, João Gama, Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning and Data Mining.

Classification Tree

John Case, Sanjay Jain, Jean-Michel Renders, 2010, Encyclopedia of Machine Learning.

K. Ting, C. Tan, S. Wei, 2007 .

Predictive software models

Stan Matwin, Eyke Hüllermeier, Johannes Fürnkranz, 2004, Conference on Semantics in Text Processing.

Sensitivity and Specificity

Kai Ming Ting, K. Ting, 2017, Encyclopedia of Machine Learning and Data Mining.

Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

Z. Liu, K. Ting, Sunil Aryal, 2021 .

Complex Adaptive System

John Case, Jean-Michel Renders, Eamonn J. Keogh, 2010, Encyclopedia of Machine Learning.

Efficient serial and parallel implementations of the cutting angle global optimisation technique

K. Ting, G. Beliakov, M. Murshed, 2001 .

Precision and Recall

K. Ting, 2017, Encyclopedia of Machine Learning and Data Mining.

Explanation

Subbarao Kambhampati, Krzysztof Krawiec, Moshe Sipper, 1953, Cladistics : the international journal of the Willi Hennig Society.

Spectral–Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest

Z. Liu, K. Ting, Sunil Aryal, 2021, IEEE Transactions on Geoscience and Remote Sensing.

Precision and Recall

Kai Ming Ting, K. Ting, 2017, Encyclopedia of Machine Learning and Data Mining.

Precision and Recall

Kai Ming Ting, K. Ting, 2017, Encyclopedia of Machine Learning and Data Mining.

A Comparative Study of Cost-Sensitive Boosting Algorithms

Kai Ming Ting, K. Ting, 2000, ICML.

A New Distributional Treatment for Time Series and An Anomaly Detection Investigation

K. Ting, Ye Zhu, Zongyou Liu, 2022, Proc. VLDB Endow..

Certainty Equivalence Principle

Jean-Michel Renders, João Gama, Lise Getoor, 2010, Encyclopedia of Machine Learning and Data Mining.

Semantic Mapping

Risto Miikkulainen, Scott Sanner, Kristian Kersting, 2010, Encyclopedia of Machine Learning.

A comparative study of data-dependent approaches without learning in measuring similarities of data objects

K. Ting, Gholamreza Haffari, Sunil Aryal, 2019, Data Mining and Knowledge Discovery.

Reconstruction-based Anomaly Detection with Completely Random Forest

Zhi-Hua Zhou, Yuan Jiang, Kai Ming Ting, 2021, SDM.

Detecting Change Intervals with Isolation Distributional Kernel

Flora D. Salim, K. Ting, Ye Zhu, 2022, ArXiv.

Issues in Classifier Evaluation using Optimal Cost Curves

Kai Ming Ting, K. Ting, 2002, ICML.

Spectrum of Variable-Random Trees

Yang Yu, Zhi-Hua Zhou, Kai Ming Ting, 2008, J. Artif. Intell. Res..

Hierarchical clustering that takes advantage of both density-peak and density-connectivity pdf

Kai Ming Ting, Maia Angelova, Yuan Jin, 2018, Inf. Syst..

Grouping points by shared subspaces for effective subspace clustering

Kai Ming Ting, Ye Zhu, Mark J. Carman, 2018, Pattern Recognit..

Isolation-Based Anomaly Detection

Fei Tony Liu, FEI TONY LIU, KAI MING TING, 2012, TKDD.

On Low-Rank Regularized Least Squares for Scalable Nonlinear Classification

Zhouyu Fu, Guojun Lu, Kai Ming Ting, 2011, ICONIP.

Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning

Zhi-Hua Zhou, Kai Ming Ting, Yue Zhu, 2017, AAAI.

An Instance-weighting Method to Induce Cost-sensitive Trees

Kai Ming Ting, K. Ting, 2001 .

Isolation Forest

Zhi-Hua Zhou, Kai Ming Ting, Fei Tony Liu, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Cocktail Ensemble for Regression

Yang Yu, Zhi-Hua Zhou, Kai Ming Ting, 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

Confusion Matrix

Kai Ming Ting, K. Ting, 2010, Encyclopedia of Machine Learning and Data Mining.

The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms pdf

Kai Ming Ting, Xin Han, Gang Li, 2020, Pattern Recognit..

Evaluation Data

Joshua D. Knowles, S. Kambhampati, T. Heskes, 2010, Encyclopedia of Machine Learning.

A Survey of Audio-Based Music Classification and Annotation

Zhouyu Fu, Guojun Lu, Kai Ming Ting, 2011, IEEE Transactions on Multimedia.

Issues in Stacked Generalization

Ian H. Witten, Kai Ming Ting, I. Witten, 2011, J. Artif. Intell. Res..

Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification

Geoffrey I. Webb, Kai Ming Ting, Fei Zheng, 2011, Machine Learning.

Improving Deep Forest by Confidence Screening

Zhi-Hua Zhou, Peng Zhao, Ming Pang, 2018, 2018 IEEE International Conference on Data Mining (ICDM).

A non-time series approach to vehicle related time series problems

Kai Ming Ting, Naiwala P. Chandrasiri, Jonathan R. Wells, 2012, AusDM.

Model stability: a key factor in determining whether an algorithm produces an optimal model from a matching distribution

Kai Ming Ting, Regina Jing Ying Quek, K. Ting, 2003, Third IEEE International Conference on Data Mining.

CDF Transform-Shift: An effective way to deal with inhomogeneous density datasets pdf

Kai Ming Ting, Maia Angelova, Ye Zhu, 2018, ArXiv.

LiNearN: A new approach to nearest neighbour density estimator

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, 2014, Pattern Recognit..

A Study on the Effect of Class Distribution Using Cost-Sensitive Learning

Kai Ming Ting, K. Ting, 2002, Discovery Science.

Learning Lazy Rules to Improve the Performance of Classifiers

Geoffrey I. Webb, Kai Ming Ting, Zijian Zheng, 2000 .

Boosting Cost-Sensitive Trees

Kai Ming Ting, Zijian Zheng, K. Ting, 1998, Discovery Science.

Isolation Kernel Estimators

Jonathan R. Wells, K. Ting, Ye Zhu, 2022, Knowledge and Information Systems.

Isolation Kernel Density Estimation

Jonathan R. Wells, K. Ting, Takashi Washio, 2021, 2021 IEEE International Conference on Data Mining (ICDM).

Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection

Zhi-Hua Zhou, Kai Ming Ting, Takashi Washio, 2020, KDD.

Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering

Kai Ming Ting, Xiaoyu Qin, Ye Zhu, 2019, AAAI.

Isolation Kernel and Its Effect on SVM

Zhi-Hua Zhou, Kai Ming Ting, Yue Zhu, 2018, KDD.

Fast Anomaly Detection for Streaming Data

Kai Ming Ting, Swee Chuan Tan, Fei Tony Liu, 2011, IJCAI.

A comparative study of data-dependent approaches without learning in measuring similarities of data objects

Gholamreza Haffari, Kai Ming Ting, Sunil Aryal, 2020, Data Mining and Knowledge Discovery.

Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure

Zhi-Hua Zhou, Kai Ming Ting, Mark James Carman, 2016, KDD.

Gholamreza Haffari, Kai Ming Ting, Takashi Washio, 2014, 2014 IEEE International Conference on Data Mining.

Feature-subspace aggregating: ensembles for stable and unstable learners

Geoffrey I. Webb, Kai Ming Ting, Swee Chuan Tan, 2011, Machine Learning.

On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions

Geoffrey I. Webb, Kai Ming Ting, K. Ting, 2005, Machine Learning.

Multi-dimensional Mass Estimation and Mass-based Clustering

Kai Ming Ting, Jonathan R. Wells, K. Ting, 2010, 2010 IEEE International Conference on Data Mining.

Evolutionary Computing

Thomas E. Potok, Joshua D. Knowles, Robert M. Patton, 2019, Encyclopedia of Machine Learning and Data Mining.

Breaking the curse of dimensionality with Isolation Kernel pdf

Kai Ming Ting, Ye Zhu, Takashi Washio, 2021, ArXiv.

CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities

Mark J. Carman, K. Ting, M. Angelova, 2018, Pattern Recognit..

A new simple and efficient density estimator that enables fast systematic search

Kai Ming Ting, Jonathan R. Wells, K. Ting, 2019, Pattern Recognit. Lett..

A simple efficient density estimator that enables fast systematic search pdf

Kai Ming Ting, Jonathan R. Wells, K. Ting, 2017, ArXiv.

Learning from Batched Data: Model Combination Versus Data Combination

Ian H. Witten, Kai Ming Ting, Boon Toh Low, 1999, Knowledge and Information Systems.

Model Combination in the Multiple-Data-Batches Scenario

Kai Ming Ting, Boon Toh Low, K. Ting, 1997, ECML.

Cooperative Coevolution

Charles X. Ling, T. Shultz, A. Mueen, 2010, Encyclopedia of Machine Learning and Data Mining.

Continual Learning

Charles X. Ling, T. Shultz, A. Mueen, 2010, Encyclopedia of Machine Learning.

Density Estimation Based on Mass

Kai Ming Ting, Takashi Washio, Fei Tony Liu, 2011, 2011 IEEE 11th International Conference on Data Mining.

Mass estimation and its applications

Kai Ming Ting, Swee Chuan Tan, Fei Tony Liu, 2010, KDD.

Density-ratio based clustering for discovering clusters with varying densities

Kai Ming Ting, Mark James Carman, Ye Zhu, 2016, Pattern Recognit..

z-SVM: An SVM for Improved Classification of Imbalanced Data

Joarder Kamruzzaman, Kai Ming Ting, Tasadduq Imam, 2006, Australian Conference on Artificial Intelligence.

Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees

Zhi-Hua Zhou, Kai Ming Ting, Xin Mu, 2016, IEEE Transactions on Knowledge and Data Engineering.

Discretisation in Lazy Learning Algorithms

Kai Ming Ting, K. Ting, 1997, Artificial Intelligence Review.

Sequential Data

Petra Kralj Novak, T. Zeugmann, K. Kersting, 2010, Encyclopedia of Machine Learning.

To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles

Geoffrey I. Webb, Kevin B. Korb, Kai Ming Ting, 2006, ECML.

Inducing Cost-Sensitive Trees via Instance Weighting

Kai Ming Ting, K. Ting, 1998, PKDD.

Boosting Trees for Cost-Sensitive Classifications

Kai Ming Ting, Zijian Zheng, K. Ting, 1998, ECML.

Theory combination: an alternative to data combination

Kai Ming Ting, Boon Toh Low, K. Ting, 1996 .

Multi-Label Learning with Emerging New Labels

Zhi-Hua Zhou, Kai Ming Ting, Yue Zhu, 2018, IEEE Transactions on Knowledge and Data Engineering.

Stacking Bagged and Dagged Models

Ian H. Witten, Kai Ming Ting, I. Witten, 1997, ICML.

Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes

Yuan Jiang, Peng Zhao, Xin Mu, 2019, 2019 IEEE International Conference on Data Mining (ICDM).

DEMass: a new density estimator for big data

Fei Tony Liu, Jonathan R. Wells, K. Ting, 2013, Knowledge and Information Systems.

Isolation‐based anomaly detection using nearest‐neighbor ensembles

Kai Ming Ting, David W. Albrecht, Tharindu R. Bandaragoda, 2018, Comput. Intell..

Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms

Mark J. Carman, Zhi-Hua Zhou, K. Ting, 2018, Machine Learning.

A Generic Ensemble Approach to Estimate Multidimensional Likelihood in Bayesian Classifier Learning

Kai Ming Ting, Sunil Aryal, K. Ting, 2016, Comput. Intell..

Improving iForest with Relative Mass

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, 2014, PAKDD.

On Detecting Clustered Anomalies Using SCiForest

Zhi-Hua Zhou, Kai Ming Ting, Fei Tony Liu, 2010, ECML/PKDD.

Mass estimation

Kai Ming Ting, Swee Chuan Tan, Fei Tony Liu, 2012, Machine Learning.

A new simple and effective measure for bag-of-word inter-document similarity measurement pdf

Gholamreza Haffari, Kai Ming Ting, Takashi Washio, 2019, ArXiv.

Beyond tf-idf and Cosine Distance in Documents Dissimilarity Measure

Gholamreza Haffari, Kai Ming Ting, Takashi Washio, 2015, AIRS.

Error Rate

Kai Ming Ting, K. Ting, 2010, Encyclopedia of Machine Learning.

A Study of AdaBoost with Naive Bayesian Classifiers: Weakness and Improvement

Kai Ming Ting, Zijian Zheng, K. Ting, 2003, Comput. Intell..

LOCAL MODELS—THE KEY TO BOOSTING STABLE LEARNERS SUCCESSFULLY

Kai Ming Ting, Jonathan R. Wells, Lian Zhu, 2013, Comput. Intell..

LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours

Kai Ming Ting, David W. Albrecht, Guansong Pang, 2015, 2015 IEEE International Conference on Data Mining Workshop (ICDMW).

Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors

Jonathan R. Wells, K. Ting, T. Washio, 2016, Machine Learning.

Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble

Kai Ming Ting, David W. Albrecht, Tharindu R. Bandaragoda, 2014, 2014 IEEE International Conference on Data Mining Workshop.

Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel

Kai Ming Ting, Ye Zhu, K. Ting, 2019, J. Artif. Intell. Res..

Which Outlier Detector Should I use?

Kai Ming Ting, Takashi Washio, Sunil Aryal, 2018, 2018 IEEE International Conference on Data Mining (ICDM).

Improving the Effectiveness and Efﬁciency of Stochastic Neighbour Embedding With Isolation Kernel (Extended Abstract) (cid:3)

K. Ting, Ye Zhu, 2022 .

Relevance feature mapping for content-based multimedia information retrieval

Yilong Yin, Kai Ming Ting, Fei Tony Liu, 2012, Pattern Recognit..

Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees

Geoffrey I. Webb, Kai Ming Ting, Zijian Zheng, 1999, ICML.

Decision Combination Based on the Characterisation of Predictive Accuracy

Kai Ming Ting, K. Ting, 1997, Intell. Data Anal..

Multi-Label Learning with Emerging New Labels

Zhi-Hua Zhou, Kai Ming Ting, Yue Zhu, 2018, IEEE Transactions on Knowledge and Data Engineering.

Half-space mass: a maximally robust and efficient data depth method

K. Ting, Gholamreza Haffari, T. Washio, 2015, Machine Learning.

Issues of grid-cluster retrievals in swarm-based clustering

Kai Ming Ting, Swee Chuan Tan, Shyh Wei Teng, 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection pdf

Zhi-Hua Zhou, Kai Ming Ting, Takashi Washio, 2020, ArXiv.

Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning

Geoffrey I. Webb, Kai Ming Ting, Zijian Zheng, 1998, Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294).

Improving Deep Forest by Screening

Kai Ming Ting, Zhi-Hua Zhou, Ming Pang, 2020, IEEE Transactions on Knowledge and Data Engineering.

A general stochastic clustering method for automatic cluster discovery

Kai Ming Ting, Swee Chuan Tan, Shyh Wei Teng, 2011, Pattern Recognit..

Kai Ming Ting, Swee Chuan Tan, Shyh Wei Teng, 2007, ACAL.

Reproducing the Results of Ant-based Clustering Without Using Ants

Kai Ming Ting, Swee Chuan Tan, Shyh Wei Teng, 2006, 2006 IEEE International Conference on Evolutionary Computation.

Simplifying and improving ant-based clustering

Kai Ming Ting, Swee Chuan Tan, Shyh Wei Teng, 2011, ICCS.

Clustering gene expression data using ant-based heuristics

Swee Chuan Tan, Kai Ting, Shyh Teng, 2011, CEC 2011.

Error Rate

Kai Ming Ting, K. Ting, 2010, Encyclopedia of Machine Learning.

Towards a Persistence Diagram that is Robust to Noise and Varied Densities

K. Ting, Ye Zhu, Hang Zhang, 2023, ICML.

Kernel-based clustering via Isolation Distributional Kernel

K. Ting, Ye Zhu, 2023, Inf. Syst..

Local contrast as an effective means to robust clustering against varying densities

K. Ting, T. Washio, Ye Zhu, 2018, Machine Learning.

Classifying under computational resource constraints: anytime classification using probabilistic estimators

Geoffrey I. Webb, Kevin B. Korb, Kai Ming Ting, 2007, Machine Learning.

A new effective and efficient measure for outlying aspect mining

Kai Ming Ting, Durgesh Samariya, Sunil Aryal, 2020, WISE.

Explanation

Shiau Hong Lim, S. Kambhampati, T. Heskes, 1953, Cladistics : the international journal of the Willi Hennig Society.

Analysis of runtime performance using heap data structure for the cutting angle global optimisation algorithm

Kai Ming Ting, Mohammad Manzur Murshed, Jonathan R. Wells, 2003 .

Clustering based on Point-Set Kernel pdf

Kai Ming Ting, Jonathan R. Wells, Ye Zhu, 2020, ArXiv.

ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets

Huidong Jin, Kai Ming Ting, David W. Albrecht, 2016, J. Artif. Intell. Res..

Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection

Gholamreza Haffari, Kai Ming Ting, Sunil Aryal, 2016, PAISI.

Relevance feature mapping for content-based image retrieval

Yilong Yin, Kai Ming Ting, Fei Tony Liu, 2010, MDMKDD '10.

Isolation kernel: the X factor in efficient and effective large scale online kernel learning pdf

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, 2019, Data Mining and Knowledge Discovery.

Efficient nonlinear classification via low-rank regularised least squares

K. Ting, Dengsheng Zhang, Zhouyu Fu, 2012, Neural computing & applications (Print).

Building Sparse Support Vector Machines for Multi-Instance Classification

Zhouyu Fu, Guojun Lu, Kai Ming Ting, 2011, ECML/PKDD.

New Class Adaptation Via Instance Generation in One-Pass Class Incremental Learning

Zhi-Hua Zhou, Kai Ming Ting, Yue Zhu, 2017, 2017 IEEE International Conference on Data Mining (ICDM).

On Feature Combination for Music Classification

Zhouyu Fu, Guojun Lu, Kai Ming Ting, 2010, SSPR/SPR.

To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators

Geoffrey I. Webb, Kevin B. Korb, Kai Ming Ting, 2007, IEEE Transactions on Knowledge and Data Engineering.

Learning Naive Bayes Classifiers for Music Classification and Retrieval

Zhouyu Fu, Guojun Lu, Kai Ming Ting, 2010, 2010 20th International Conference on Pattern Recognition.

Confusion Matrix

K. Ting, 2010, Encyclopedia of Machine Learning.

Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract)

K. Ting, Ye Zhu, 2022, IJCAI.

Improving Stochastic Neighbour Embedding fundamentally with a well-defined data-dependent kernel pdf

Kai Ming Ting, Ye Zhu, K. Ting, 2019, ArXiv.

A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection

K. Ting, Yufan Wang, Yuanyi Shang, 2024, J. Artif. Intell. Res..

Stream Mining

Petra Kralj Novak, T. Zeugmann, K. Kersting, 2010, Encyclopedia of Machine Learning.

Detecting Change Intervals with Isolation Distributional Kernel

Flora D. Salim, K. Ting, Ye Zhu, 2022, J. Artif. Intell. Res..

Streaming Hierarchical Clustering Based on Point-Set Kernel

K. Ting, Ye Zhu, De-chuan Zhan, 2022, KDD.

Isolation Graph Kernel

Yuan Jiang, Kai Ming Ting, Bi-Cun Xu, 2021, AAAI.

Isolation Set-Kernel and Its Application to Multi-Instance Learning

Zhi-Hua Zhou, Kai Ming Ting, Bi-Cun Xu, 2019, KDD.

A Distance Scaling Method to Improve Density-Based Clustering

Kai Ming Ting, Maia Angelova, Ye Zhu, 2018, PAKDD.