# Kai Ming Ting

According to our database

Collaborative distances:

^{1}, Kai Ming Ting authored at least 109 papers between 1994 and 2018.Collaborative distances:

## Timeline

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#### On csauthors.net:

## Bibliography

2018

Multi-Label Learning with Emerging New Labels.

IEEE Trans. Knowl. Data Eng., 2018

Grouping points by shared subspaces for effective subspace clustering.

Pattern Recognition, 2018

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

Machine Learning, 2018

A Distance Scaling Method to Improve Density-Based Clustering.

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Neighbourhood Contrast: A Better Means to Detect Clusters Than Density.

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Isolation Kernel and Its Effect on SVM.

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

2017

Sensitivity and Specificity.

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Precision and Recall.

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Precision.

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Error Rate.

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Confusion Matrix.

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

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

IEEE Trans. Knowl. Data Eng., 2017

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

Machine Learning, 2017

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

Knowl. Inf. Syst., 2017

A simple efficient density estimator that enables fast systematic search.

CoRR, 2017

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

Proceedings of the 2017 IEEE International Conference on Data Mining, 2017

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

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Density-ratio based clustering for discovering clusters with varying densities.

Pattern Recognition, 2016

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

Machine Learning, 2016

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

J. Artif. Intell. Res., 2016

Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees.

CoRR, 2016

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

Computational Intelligence, 2016

Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection.

Proceedings of the Intelligence and Security Informatics - 11th Pacific Asia Workshop, 2016

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

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Multi-label Learning with Emerging New Labels.

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

2015

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

Machine Learning, 2015

LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours.

Proceedings of the IEEE International Conference on Data Mining Workshop, 2015

Beyond tf-idf and Cosine Distance in Documents Dissimilarity Measure.

Proceedings of the Information Retrieval Technology, 2015

2014

LiNearN: A new approach to nearest neighbour density estimator.

Pattern Recognition, 2014

Improving iForest with Relative Mass.

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2014

Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble.

Proceedings of the 2014 IEEE International Conference on Data Mining Workshops, 2014

Mp-Dissimilarity: A Data Dependent Dissimilarity Measure.

Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

2013

Learning Sparse Kernel Classifiers for Multi-Instance Classification.

IEEE Trans. Neural Netw. Learning Syst., 2013

Efficient nonlinear classification via low-rank regularised least squares.

Neural Computing and Applications, 2013

Mass estimation.

Machine Learning, 2013

DEMass: a new density estimator for big data.

Knowl. Inf. Syst., 2013

Local Models - the Key to Boosting Stable Learners Successfully.

Computational Intelligence, 2013

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

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2013

Optimizing Cepstral Features for Audio Classification.

Proceedings of the IJCAI 2013, 2013

2012

Isolation-Based Anomaly Detection.

TKDD, 2012

Relevance feature mapping for content-based multimedia information retrieval.

Pattern Recognition, 2012

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

Machine Learning, 2012

Learning Sparse Kernel Classifiers in the Primal.

Proceedings of the Structural, Syntactic, and Statistical Pattern Recognition, 2012

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

Proceedings of the Tenth Australasian Data Mining Conference, AusDM 2012, Sydney, 2012

2011

A Survey of Audio-Based Music Classification and Annotation.

IEEE Trans. Multimedia, 2011

Simplifying and improving ant-based clustering.

Proceedings of the International Conference on Computational Science, 2011

Music classification via the bag-of-features approach.

Pattern Recognition Letters, 2011

A general stochastic clustering method for automatic cluster discovery.

Pattern Recognition, 2011

Feature-subspace aggregating: ensembles for stable and unstable learners.

Machine Learning, 2011

Issues in Stacked Generalization

CoRR, 2011

Building Sparse Support Vector Machines for Multi-Instance Classification.

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Fast Anomaly Detection for Streaming Data.

Proceedings of the IJCAI 2011, 2011

On Low-Rank Regularized Least Squares for Scalable Nonlinear Classification.

Proceedings of the Neural Information Processing - 18th International Conference, 2011

Density Estimation Based on Mass.

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

2010

Sensitivity and Specificity.

Proceedings of the Encyclopedia of Machine Learning, 2010

Precision and Recall.

Proceedings of the Encyclopedia of Machine Learning, 2010

Precision.

Proceedings of the Encyclopedia of Machine Learning, 2010

Error Rate.

Proceedings of the Encyclopedia of Machine Learning, 2010

Confusion Matrix.

Proceedings of the Encyclopedia of Machine Learning, 2010

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

Knowl. Inf. Syst., 2010

On Feature Combination for Music Classification.

Proceedings of the Structural, 2010

On Detecting Clustered Anomalies Using SCiForest.

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Mass estimation and its applications.

Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010

Learning Naive Bayes Classifiers for Music Classification and Retrieval.

Proceedings of the 20th International Conference on Pattern Recognition, 2010

Multi-dimensional Mass Estimation and Mass-based Clustering.

Proceedings of the ICDM 2010, 2010

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

Proceedings of the AI 2010: Advances in Artificial Intelligence, 2010

2009

Boosting Support Vector Machines Successfully.

Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

FaSS: Ensembles for Stable Learners.

Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

2008

Spectrum of Variable-Random Trees.

J. Artif. Intell. Res., 2008

Isolation Forest.

Proceedings of the 8th IEEE International Conference on Data Mining (ICDM 2008), 2008

Issues of grid-cluster retrievals in swarm-based clustering.

Proceedings of the IEEE Congress on Evolutionary Computation, 2008

2007

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

IEEE Trans. Knowl. Data Eng., 2007

Classifying under computational resource constraints: anytime classification using probabilistic estimators.

Machine Learning, 2007

Cocktail Ensemble for Regression.

Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering.

Proceedings of the Progress in Artificial Life, Third Australian Conference, 2007

2006

Variable Randomness in Decision Tree Ensembles.

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2006

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

Proceedings of the Machine Learning: ECML 2006, 2006

Reproducing the Results of Ant-based Clustering Without Using Ants.

Proceedings of the IEEE International Conference on Evolutionary Computation, 2006

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

Proceedings of the AI 2006: Advances in Artificial Intelligence, 2006

Ehipasiko: A Content-based Image Indexing and Retrieval System.

Proceedings of the Advances in Intelligent IT, 2006

2005

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

Machine Learning, 2005

Maximizing Tree Diversity by Building Complete-Random Decision Trees.

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2005

Ensemble Selection for SuperParent-One-Dependence Estimators.

Proceedings of the AI 2005: Advances in Artificial Intelligence, 2005

2004

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

Proceedings of the Machine Learning: ECML 2004, 2004

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

Proceedings of the AI 2004: Advances in Artificial Intelligence, 2004

2003

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

Computational Intelligence, 2003

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

Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM 2003), 2003

2002

An Instance-Weighting Method to Induce Cost-Sensitive Trees.

IEEE Trans. Knowl. Data Eng., 2002

Issues in Classifier Evaluation using Optimal Cost Curves.

Proceedings of the Machine Learning, 2002

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

Proceedings of the Discovery Science, 5th International Conference, 2002

2000

A Comparative Study of Cost-Sensitive Boosting Algorithms.

Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29, 2000

An Empirical Study of MetaCost Using Boosting Algorithms.

Proceedings of the Machine Learning: ECML 2000, 11th European Conference on Machine Learning, Barcelona, Catalonia, Spain, May 31, 2000

1999

Learning from Batched Data: Model Combination Versus Data Combination.

Knowl. Inf. Syst., 1999

Issues in Stacked Generalization.

J. Artif. Intell. Res., 1999

Improving the Performance of Boosting for Naive Bayesian Classification.

Proceedings of the Methodologies for Knowledge Discovery and Data Mining, 1999

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

Proceedings of the International Joint Conference Neural Networks, 1999

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

Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27, 1999

1998

Inducing Cost-Sensitive Trees via Instance Weighting.

Proceedings of the Principles of Data Mining and Knowledge Discovery, 1998

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

Proceedings of the Tenth IEEE International Conference on Tools with Artificial Intelligence, 1998

Boosting Trees for Cost-Sensitive Classifications.

Proceedings of the Machine Learning: ECML-98, 1998

Boosting Cost-Sensitive Trees.

Proceedings of the Discovery Science, 1998

1997

Decision Combination Based on the Characterisation of Predictive Accuracy.

Intell. Data Anal., 1997

Discretisation in Lazy Learning Algorithms.

Artif. Intell. Rev., 1997

Stacked Generalizations: When Does It Work?

Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1997

Stacking Bagged and Dagged Models.

Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), 1997

Model Combination in the Multiple-Data-Batches Scenario.

Proceedings of the Machine Learning: ECML-97, 1997

1996

The Characterisation of Predictive Accuracy and Decision Combination.

Proceedings of the Machine Learning, 1996

1995

Towards using a Single Uniform Metric in Instance-Based Learning.

Proceedings of the Case-Based Reasoning Research and Development, 1995

1994

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

Proceedings of the 27th Annual Hawaii International Conference on System Sciences (HICSS-27), 1994