Google
Google Scholar
MS Academic
CiteSeerX
CORE
Semantic ScholarKai Ming Ting
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2010 – today
- 2018
[j36]Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Ye Zhu, Jonathan R. Wells:
Isolation-based anomaly detection using nearest-neighbor ensembles. Computational Intelligence 34(4): 968-998 (2018)- [j35]Bo Chen, Kai Ming Ting, Takashi Washio, Ye Zhu:
Local contrast as an effective means to robust clustering against varying densities. Machine Learning 107(8-10): 1621-1645 (2018) - [j34]Ye Zhu, Kai Ming Ting, Mark J. Carman:
Grouping points by shared subspaces for effective subspace clustering. Pattern Recognition 83: 230-244 (2018) - [j33]Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou:
Multi-Label Learning with Emerging New Labels. IEEE Trans. Knowl. Data Eng. 30(10): 1901-1914 (2018) - [c62]
- [c61]
- [c60]Ye Zhu, Kai Ming Ting, Maia Angelova:
A Distance Scaling Method to Improve Density-Based Clustering. PAKDD (3) 2018: 389-400 - [c59]Bo Chen, Kai Ming Ting:
Neighbourhood Contrast: A Better Means to Detect Clusters Than Density. PAKDD (3) 2018: 401-412 - [i5]Ye Zhu, Kai Ming Ting, Mark J. Carman, Maia Angelova:
CDF Transform-Shift: An effective way to deal with inhomogeneous density datasets. CoRR abs/1810.02897 (2018) - [i4]Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova:
Hierarchical clustering that takes advantage of both density-peak and density-connectivity. CoRR abs/1810.03393 (2018) - 2017
- [j32]Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari:
Data-dependent dissimilarity measure: an effective alternative to geometric distance measures. Knowl. Inf. Syst. 53(2): 479-506 (2017) - [j31]Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Sunil Aryal:
Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors. Machine Learning 106(1): 55-91 (2017) - [j30]Xin Mu, Kai Ming Ting, Zhi-Hua Zhou:
Classification Under Streaming Emerging New Classes: A Solution Using Completely-Random Trees. IEEE Trans. Knowl. Data Eng. 29(8): 1605-1618 (2017) - [c58]Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou:
Discover Multiple Novel Labels in Multi-Instance Multi-Label Learning. AAAI 2017: 2977-2984 - [c57]Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou:
New Class Adaptation Via Instance Generation in One-Pass Class Incremental Learning. ICDM 2017: 1207-1212 - [r10]
- [r9]
- [r8]
- [r7]
- [r6]Kai Ming Ting:
Sensitivity and Specificity. Encyclopedia of Machine Learning and Data Mining 2017: 1152 - [i3]Jonathan R. Wells, Kai Ming Ting:
A simple efficient density estimator that enables fast systematic search. CoRR abs/1707.00783 (2017) - 2016
- [j29]Sunil Aryal, Kai Ming Ting:
A Generic Ensemble Approach to Estimate Multidimensional Likelihood in Bayesian Classifier Learning. Computational Intelligence 32(3): 458-479 (2016) - [j28]Guansong Pang, Kai Ming Ting, David W. Albrecht, Huidong Jin:
ZERO++: Harnessing the Power of Zero Appearances to Detect Anomalies in Large-Scale Data Sets. J. Artif. Intell. Res. 57: 593-620 (2016) - [j27]Ye Zhu, Kai Ming Ting:
Commentary: a decomposition of the outlier detection problem into a set of supervised learning problems. Machine Learning 105(2): 301-304 (2016) - [j26]Ye Zhu, Kai Ming Ting, Mark James Carman:
Density-ratio based clustering for discovering clusters with varying densities. Pattern Recognition 60: 983-997 (2016) - [c56]Yue Zhu, Kai Ming Ting, Zhi-Hua Zhou:
Multi-label Learning with Emerging New Labels. ICDM 2016: 1371-1376 - [c55]Kai Ming Ting, Ye Zhu, Mark James Carman, Yue Zhu, Zhi-Hua Zhou:
Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure. KDD 2016: 1205-1214 - [c54]Sunil Aryal, Kai Ming Ting, Gholamreza Haffari:
Revisiting Attribute Independence Assumption in Probabilistic Unsupervised Anomaly Detection. PAISI 2016: 73-86 - [i2]Xin Mu, Kai Ming Ting, Zhi-Hua Zhou:
Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees. CoRR abs/1605.09131 (2016) - 2015
- [j25]Bo Chen, Kai Ming Ting, Takashi Washio, Gholamreza Haffari:
Half-space mass: a maximally robust and efficient data depth method. Machine Learning 100(2-3): 677-699 (2015) - [c53]Sunil Aryal, Kai Ming Ting, Gholamreza Haffari, Takashi Washio:
Beyond tf-idf and Cosine Distance in Documents Dissimilarity Measure. AIRS 2015: 400-406 - [c52]Guansong Pang, Kai Ming Ting, David W. Albrecht:
LeSiNN: Detecting Anomalies by Identifying Least Similar Nearest Neighbours. ICDM Workshops 2015: 623-630 - 2014
- [j24]Jonathan R. Wells, Kai Ming Ting, Takashi Washio:
LiNearN: A new approach to nearest neighbour density estimator. Pattern Recognition 47(8): 2702-2720 (2014) - [c51]Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Jonathan R. Wells:
Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble. ICDM Workshops 2014: 698-705 - [c50]Sunil Aryal, Kai Ming Ting, Gholamreza Haffari, Takashi Washio:
Mp-Dissimilarity: A Data Dependent Dissimilarity Measure. ICDM 2014: 707-712 - [c49]Sunil Aryal, Kai Ming Ting, Jonathan R. Wells, Takashi Washio:
Improving iForest with Relative Mass. PAKDD (2) 2014: 510-521 - 2013
- [j23]Kai Ming Ting, Lian Zhu, Jonathan R. Wells:
Local Models - the Key to Boosting Stable Learners Successfully. Computational Intelligence 29(2): 331-356 (2013) - [j22]Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu, Sunil Aryal:
DEMass: a new density estimator for big data. Knowl. Inf. Syst. 35(3): 493-524 (2013) - [j21]Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, Swee Chuan Tan:
Mass estimation. Machine Learning 90(1): 127-160 (2013) - [j20]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
Efficient nonlinear classification via low-rank regularised least squares. Neural Computing and Applications 22(7-8): 1279-1289 (2013) - [j19]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
Learning Sparse Kernel Classifiers for Multi-Instance Classification. IEEE Trans. Neural Netw. Learning Syst. 24(9): 1377-1389 (2013) - [c48]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
Optimizing Cepstral Features for Audio Classification. IJCAI 2013: 1330-1336 - [c47]Sunil Aryal, Kai Ming Ting:
MassBayes: A New Generative Classifier with Multi-dimensional Likelihood Estimation. PAKDD (1) 2013: 136-148 - 2012
- [j18]Geoffrey I. Webb, Janice R. Boughton, Fei Zheng, Kai Ming Ting, Houssam Salem:
Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification. Machine Learning 86(2): 233-272 (2012) - [j17]Guang-Tong Zhou, Kai Ming Ting, Fei Tony Liu, Yilong Yin:
Relevance feature mapping for content-based multimedia information retrieval. Pattern Recognition 45(4): 1707-1720 (2012) - [j16]Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou:
Isolation-Based Anomaly Detection. TKDD 6(1): 3:1-3:39 (2012) - [c46]Jonathan R. Wells, Kai Ming Ting, Naiwala P. Chandrasiri:
A non-time series approach to vehicle related time series problems. AusDM 2012: 61-70 - [c45]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
Learning Sparse Kernel Classifiers in the Primal. SSPR/SPR 2012: 60-69 - 2011
- [j15]Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb:
Feature-subspace aggregating: ensembles for stable and unstable learners. Machine Learning 82(3): 375-397 (2011) - [j14]Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng:
A general stochastic clustering method for automatic cluster discovery. Pattern Recognition 44(10-11): 2786-2799 (2011) - [j13]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
Music classification via the bag-of-features approach. Pattern Recognition Letters 32(14): 1768-1777 (2011) - [j12]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
A Survey of Audio-Based Music Classification and Annotation. IEEE Trans. Multimedia 13(2): 303-319 (2011) - [c44]Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu:
Density Estimation Based on Mass. ICDM 2011: 715-724 - [c43]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
On Low-Rank Regularized Least Squares for Scalable Nonlinear Classification. ICONIP (2) 2011: 490-499 - [c42]Swee Chuan Tan, Kai Ming Ting, Fei Tony Liu:
Fast Anomaly Detection for Streaming Data. IJCAI 2011: 1511-1516 - [c41]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
Building Sparse Support Vector Machines for Multi-Instance Classification. ECML/PKDD (1) 2011: 471-486 - [c40]Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng:
Simplifying and improving ant-based clustering. ICCS 2011: 46-55 - [i1]
- 2010
- [j11]Takashi Washio, Einoshin Suzuki, Kai Ming Ting:
Best papers from the 12th Pacific-Asia conference on knowledge discovery and data mining (PAKDD2008). Knowl. Inf. Syst. 25(2): 209-210 (2010) - [c39]Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng:
A Comparative Study of a Practical Stochastic Clustering Method with Traditional Methods. Australasian Conference on Artificial Intelligence 2010: 112-121 - [c38]Kai Ming Ting, Jonathan R. Wells:
Multi-dimensional Mass Estimation and Mass-based Clustering. ICDM 2010: 511-520 - [c37]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
Learning Naive Bayes Classifiers for Music Classification and Retrieval. ICPR 2010: 4589-4592 - [c36]Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, James Swee Chuan Tan:
Mass estimation and its applications. KDD 2010: 989-998 - [c35]Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou:
On Detecting Clustered Anomalies Using SCiForest. ECML/PKDD (2) 2010: 274-290 - [c34]Zhouyu Fu, Guojun Lu, Kai Ming Ting, Dengsheng Zhang:
On Feature Combination for Music Classification. SSPR/SPR 2010: 453-462 - [r5]
- [r4]
- [r3]
- [r2]
- [r1]
2000 – 2009
- 2009
- [c33]Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb:
FaSS: Ensembles for Stable Learners. MCS 2009: 364-374 - [c32]
- 2008
- [j10]Fei Tony Liu, Kai Ming Ting, Yang Yu, Zhi-Hua Zhou:
Spectrum of Variable-Random Trees. J. Artif. Intell. Res. 32: 355-384 (2008) - [c31]Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng:
Issues of grid-cluster retrievals in swarm-based clustering. IEEE Congress on Evolutionary Computation 2008: 511-518 - [c30]
- [e1]Takashi Washio, Einoshin Suzuki, Kai Ming Ting, Akihiro Inokuchi:
Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings. Lecture Notes in Computer Science 5012, Springer 2008, ISBN 978-3-540-68124-3 [contents] - 2007
- [j9]Ying Yang, Geoffrey I. Webb, Kevin B. Korb, Kai Ming Ting:
Classifying under computational resource constraints: anytime classification using probabilistic estimators. Machine Learning 69(1): 35-53 (2007) - [j8]Ying Yang, Geoffrey I. Webb, Jesús Cerquides, Kevin B. Korb, Janice R. Boughton, Kai Ming Ting:
To Select or To Weigh: A Comparative Study of Linear Combination Schemes for SuperParent-One-Dependence Estimators. IEEE Trans. Knowl. Data Eng. 19(12): 1652-1665 (2007) - [c29]Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng:
Examining Dissimilarity Scaling in Ant Colony Approaches to Data Clustering. ACAL 2007: 269-280 - [c28]
- 2006
- [c27]Shyh Wei Teng, Kai Ming Ting:
Ehipasiko: A Content-based Image Indexing and Retrieval System. AMT 2006: 436-437 - [c26]Tasadduq Imam, Kai Ming Ting, Joarder Kamruzzaman:
z-SVM: An SVM for Improved Classification of Imbalanced Data. Australian Conference on Artificial Intelligence 2006: 264-273 - [c25]Swee Chuan Tan, Kai Ming Ting, Shyh Wei Teng:
Reproducing the Results of Ant-based Clustering Without Using Ants. IEEE Congress on Evolutionary Computation 2006: 1760-1767 - [c24]Ying Yang, Geoffrey I. Webb, Jesús Cerquides, Kevin B. Korb, Janice R. Boughton, Kai Ming Ting:
To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. ECML 2006: 533-544 - [c23]
- 2005
- [j7]Geoffrey I. Webb, Kai Ming Ting:
On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Machine Learning 58(1): 25-32 (2005) - [c22]Ying Yang, Kevin B. Korb, Kai Ming Ting, Geoffrey I. Webb:
Ensemble Selection for SuperParent-One-Dependence Estimators. Australian Conference on Artificial Intelligence 2005: 102-112 - [c21]Fei Tony Liu, Kai Ming Ting, Wei Fan:
Maximizing Tree Diversity by Building Complete-Random Decision Trees. PAKDD 2005: 605-610 - 2004
- [c20]Kwok Pan Pang, Kai Ming Ting:
Improving the Centered CUSUMS Statistic for Structural Break Detection in Time Series. Australian Conference on Artificial Intelligence 2004: 402-413 - [c19]Kai Ming Ting:
Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees. ECML 2004: 429-440 - 2003
- [j6]Kai Ming Ting, Zijian Zheng:
A Study of AdaBoost with Naive Bayesian Classifiers: Weakness and Improvement. Computational Intelligence 19(2): 186-200 (2003) - [c18]Kai Ming Ting, Regina Jing Ying Quek:
Model Stability: A key factor in determining whether an algorithm produces an optimal model from a matching distribution. ICDM 2003: 653-656 - 2002
- [j5]Kai Ming Ting:
An Instance-Weighting Method to Induce Cost-Sensitive Trees. IEEE Trans. Knowl. Data Eng. 14(3): 659-665 (2002) - [c17]Kai Ming Ting:
A Study on the Effect of Class Distribution Using Cost-Sensitive Learning. Discovery Science 2002: 98-112 - [c16]
- 2000
- [c15]
- [c14]
1990 – 1999
- 1999
- [j4]Kai Ming Ting, Ian H. Witten:
Issues in Stacked Generalization. J. Artif. Intell. Res. 10: 271-289 (1999) - [j3]Kai Ming Ting, Boon Toh Low, Ian H. Witten:
Learning from Batched Data: Model Combination Versus Data Combination. Knowl. Inf. Syst. 1(1): 83-106 (1999) - [c13]Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting:
Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. ICML 1999: 493-502 - [c12]Yakov Frayman, Kai Ming Ting, Lipo Wang:
A fuzzy neural network for data mining: dealing with the problem of small disjuncts. IJCNN 1999: 2490-2493 - [c11]Kai Ming Ting, Zijian Zheng:
Improving the Performance of Boosting for Naive Bayesian Classification. PAKDD 1999: 296-305 - 1998
- [c10]
- [c9]
- [c8]Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting:
Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning. ICTAI 1998: 216-223 - [c7]
- 1997
- [j2]Kai Ming Ting:
Discretisation in Lazy Learning Algorithms. Artif. Intell. Rev. 11(1-5): 157-174 (1997) - [j1]Kai Ming Ting:
Decision Combination Based on the Characterisation of Predictive Accuracy. Intell. Data Anal. 1(1-4): 181-205 (1997) - [c6]Kai Ming Ting, Boon Toh Low:
Model Combination in the Multiple-Data-Batches Scenario. ECML 1997: 250-265 - [c5]
- [c4]
- 1996
- [c3]Kai Ming Ting:
The Characterisation of Predictive Accuracy and Decision Combination. ICML 1996: 498-506 - 1995
- [c2]Kai Ming Ting:
Towards using a Single Uniform Metric in Instance-Based Learning. ICCBR 1995: 559-568 - 1994
- [c1]Kai Ming Ting:
An M-of-N Rule Induction Algorithm and its Application to DNA Domain. HICSS (5) 1994: 133-140
Coauthor Index
Swee Chuan Tan
aka: James Swee Chuan Tan
aka: James Swee Chuan Tan
maintained by 
, founded at 
, founded at 
last updated on 2019-02-14 22:47 CET by the dblp team
data released under the ODC-BY 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
the dblp computer science bibliography is funded by:
