Kai Ming Ting

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

Isolation Kernel and Its Effect on SVM

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

Grouping points by shared subspaces for effective subspace clustering

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

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

Kai Ming Ting, Takashi Washio, Bo Chen, 2017, Machine Learning.

Sensitivity and Specificity

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

Error Rate

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

Stacked generalization: when does it work?

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

Precision and Recall

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

An M-of-N Rule Induction Algorithm and its Application to DNA Domain

Kai Ming Ting, 1994, HICSS.

Discretisation in Lazy Learning Algorithms

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

Classifying under computational resource constraints: anytime classification using probabilistic estimators

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

Variable Randomness in Decision Tree Ensembles

Kai Ming Ting, Fei Tony Liu, 2006, PAKDD.

Have we overestimated the value of ROC analysis under varying class distributions

Kai Ming Ting, Geofirey I. Webb, 2003 .

A Distance Scaling Method to Improve Density-Based Clustering

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

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

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

On Low-Rank Regularized Least Squares for Scalable Nonlinear Classification

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

Density-ratio based clustering for discovering clusters with varying densities

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

Feature-subspace aggregating: ensembles for stable and unstable learners

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

Best papers from the 12th Pacific-Asia conference on knowledge discovery and data mining (PAKDD2008)

Kai Ming Ting, Takashi Washio, Einoshin Suzuki, 2010, Knowledge and Information Systems.

Boosting Trees for Cost-Sensitive Classifications

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

Improving iForest with Relative Mass

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

A fuzzy neural network for data mining: dealing with the problem of small disjuncts

Kai Ming Ting, Yakov Frayman, Lipo Wang, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

On Feature Combination for Music Classification

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

A new effective and efficient measure for outlying aspect mining

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

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.

The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms pdf

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

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

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

Theory combination: an alternative to data combination

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

Precision and Recall

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

Learning Sparse Kernel Classifiers for Multi-Instance Classification

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

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.

Relevance feature mapping for content-based multimedia information retrieval

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

An Empirical Study of MetaCost Using Boosting Algorithms

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

Issues in Classifier Evaluation using Optimal Cost Curves

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

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).

Building Sparse Support Vector Machines for Multi-Instance Classification

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

Density Estimation Based on Mass

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

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).

Clustering based on Point-Set Kernel pdf

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

Decision Combination Based on the Characterisation of Predictive Accuracy

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

Error Rate

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

Mass estimation and its applications

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

Commentary: a decomposition of the outlier detection problem into a set of supervised learning problems

Kai Ming Ting, Ye Zhu, 2016, Machine Learning.

Maximizing Tree Diversity by Building Complete-Random Decision Trees

Kai Ming Ting, Wei Fan, Fei Tony Liu, 2005, PAKDD.

DEMass: a new density estimator for big data

Kai Ming Ting, Takashi Washio, Fei Tony Liu, 2013, Knowledge and Information Systems.

Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning

Kai Ming Ting, Jonathan R. Wells, Sunil Aryal, 2020, Knowledge and Information Systems.

Beyond tf-idf and Cosine Distance in Documents Dissimilarity Measure

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

Model-based clustering of sequential data

Kai Ming Ting, David W. Albrecht, David L. Dowe, 2006 .

A Comparative Study of Cost-Sensitive Boosting Algorithms

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

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.

Neighbourhood Contrast: A Better Means to Detect Clusters Than Density

Kai Ming Ting, Bo Chen, 2018, PAKDD.

Learning Sparse Kernel Classifiers in the Primal

Zhouyu Fu, Guojun Lu, Kai Ming Ting, 2012, SSPR/SPR.

Sensitivity and Specificity

Kai Ming Ting, R. Mould, 1976 .

A Comparative Study of a Practical Stochastic Clustering Method with Traditional Methods

Kai Ming Ting, Swee Chuan Tan, Shyh Wei Teng, 2010, Australasian Conference on Artificial Intelligence.

Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings

Kai Ming Ting, Einoshin Suzuki, Akihiro Inokuchi, 2008, PAKDD.

Multi-dimensional Mass Estimation and Mass-based Clustering

Kai Ming Ting, Jonathan R. Wells, K. Ting, 2010, 2010 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).

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.

An Instance-weighting Method to Induce Cost-sensitive Trees

Kai Ming Ting, K. Ting, 2001 .

Spectrum of Variable-Random Trees

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

Ehipasiko: A Content-based Image Indexing and Retrieval System

Kai Ming Ting, Shyh Wei Teng, 2006, AMT.

Which Outlier Detector Should I use?

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

Issues in Stacked Generalization

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

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 .

Precision and Recall

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

Anomaly Detection via Neighbourhood Contrast

Bo Chen, Tat-Jun Chin, Kai Ming Ting, 2020, PAKDD.

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

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

A Survey of Audio-Based Music Classification and Annotation

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

A simple efficient density estimator that enables fast systematic search pdf

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

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

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

Model Combination in the Multiple-Data-Batches Scenario

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

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

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

Efficient nonlinear classification via low-rank regularised least squares

Zhouyu Fu, Guojun Lu, Kai Ming Ting, 2012, Neural Computing and Applications.

Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees

Kai Ming Ting, 2004, ECML.

LiNearN: A new approach to nearest neighbour density estimator

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

On Detecting Clustered Anomalies Using SCiForest

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

Multi-Label Learning with Emerging New Labels

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

Towards using a Single Uniform Metric in Instance-Based Learning

Kai Ming Ting, 1995, ICCBR.

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

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

Simplifying and improving ant-based clustering

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

Relevance feature mapping for content-based image retrieval

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

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

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

Inducing Cost-Sensitive Trees via Instance Weighting

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

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

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

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.

MassBayes: A New Generative Classifier with Multi-dimensional Likelihood Estimation

Kai Ming Ting, Sunil Aryal, 2013, PAKDD.

Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection

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

Sensitivity and Specificity

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

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

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

Fast Anomaly Detection for Streaming Data

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

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

Boosting Support Vector Machines Successfully

Kai Ming Ting, Lian Zhu, 2009, MCS.

Cost-Sensitive Classification Using Decision Trees, Boosting and MetaCost

Kai Ming Ting, 2002 .

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.

Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining

Kai Ming Ting, Takashi Washio, Akihiro Inokuchi, 2008 .

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

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

Learning Lazy Rules to Improve the Performance of Classifiers

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

Discretization of Continuous-Valued Attributes and Instance-Based Learning

Kai Ming Ting, K. Ting, 1994 .

Learning from Batched Data: Model Combination Versus Data Combination

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

Improving time series prediction by data selection

Kai Ming Ting, Kwok Pan Pang, 2003 .

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.

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

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

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..

Ensemble Selection for SuperParent-One-Dependence Estimators

Geoffrey I. Webb, Kevin B. Korb, Kai Ming Ting, 2005, Australian Conference on Artificial Intelligence.

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

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

Improving the Centered CUSUMS Statistic for Structural Break Detection in Time Series

Kai Ming Ting, Kwok Pan Pang, 2004, Australian Conference on Artificial Intelligence.

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

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

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.

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

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

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

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

Isolation Forest

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

Stacking Bagged and Dagged Models

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

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).

LOCAL MODELS—THE KEY TO BOOSTING STABLE LEARNERS SUCCESSFULLY

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

Multi-Label Learning with Emerging New Labels

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

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

Improving Deep Forest by Screening

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

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.