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- affiliation: Nanjing University, National Key Laboratory for Novel Software Technology, Nanjing, China
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2020 – today
- 2025
[j57]Yang Cao, Yixiao Ma, Ye Zhu, Kai Ming Ting:
Revisiting streaming anomaly detection: benchmark and evaluation. Artif. Intell. Rev. 58(1): 8 (2025)- 2024
- [j56]Kai Ming Ting, Takashi Washio, Ye Zhu
, Yang Xu, Kaifeng Zhang:
Is it possible to find the single nearest neighbor of a query in high dimensions? Artif. Intell. 336: 104206 (2024) - [j55]Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li:
Detecting Change Intervals with Isolation Distributional Kernel. J. Artif. Intell. Res. 79: 273-306 (2024) - [j54]Yufan Wang, Zijing Wang, Kai Ming Ting, Yuanyi Shang:
A Principled Distributional Approach to Trajectory Similarity Measurement and its Application to Anomaly Detection. J. Artif. Intell. Res. 79: 865-893 (2024) - [j53]Kai Ming Ting, Zongyou Liu, Lei Gong, Hang Zhang, Ye Zhu:
A new distributional treatment for time series anomaly detection. VLDB J. 33(3): 753-780 (2024) - [c80]Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Luxing Yang, Gang Li:
Detecting Change Intervalswith Isolation Distributional Kernel (Abstract Reprint). IJCAI 2024: 8476 - [c79]Lei Gong, Hang Zhang, Zongyou Liu, Kai Ming Ting, Yang Cao, Ye Zhu:
Local Subsequence-Based Distribution for Time Series Clustering. PAKDD (1) 2024: 259-270 - [c78]Yuanyi Shang, Kai Ming Ting, Zijing Wang, Yufan Wang:
Distributional Kernel: An Effective and Efficient Means for Trajectory Retrieval. PAKDD (5) 2024: 271-283 - [i22]Yang Cao, Haolong Xiang, Hang Zhang, Ye Zhu, Kai Ming Ting:
Anomaly Detection Based on Isolation Mechanisms: A Survey. CoRR abs/2403.10802 (2024) - [i21]Hang Zhang, Yang Xu, Lei Gong, Ye Zhu, Kai Ming Ting:
Distributed Clustering based on Distributional Kernel. CoRR abs/2409.09418 (2024) - [i20]Kaichen Zhou, Yang Cao, Taewhan Kim, Hao Zhao, Hao Dong, Kai Ming Ting, Ye Zhu:
RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations. CoRR abs/2410.00713 (2024) - [i19]Xinpeng Li, Zile Jiang, Kai Ming Ting, Ye Zhu:
An Online Automatic Modulation Classification Scheme Based on Isolation Distributional Kernel. CoRR abs/2410.02750 (2024) - 2023
- [j52]Ye Zhu, Kai Ming Ting:
Kernel-based clustering via Isolation Distributional Kernel. Inf. Syst. 117: 102212 (2023) - [j51]Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang, Ye Zhu:
Isolation Kernel Estimators. Knowl. Inf. Syst. 65(2): 759-787 (2023) - [j50]Xin Han, Ye Zhu, Kai Ming Ting, Gang Li:
The impact of isolation kernel on agglomerative hierarchical clustering algorithms. Pattern Recognit. 139: 109517 (2023) - [j49]Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou:
Isolation Distributional Kernel: A New Tool for Point and Group Anomaly Detections. IEEE Trans. Knowl. Data Eng. 35(3): 2697-2710 (2023) - [j48]Kai Ming Ting, Jonathan R. Wells, Ye Zhu:
Point-Set Kernel Clustering. IEEE Trans. Knowl. Data Eng. 35(5): 5147-5158 (2023) - [c77]Zijing Wang, Ye Zhu, Kai Ming Ting:
Distribution-Based Trajectory Clustering. ICDM 2023: 1379-1384 - [c76]Hang Zhang, Kaifeng Zhang, Kai Ming Ting, Ye Zhu:
Towards a Persistence Diagram that is Robust to Noise and Varied Densities. ICML 2023: 41952-41972 - [c75]Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song:
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. SDM 2023: 703-711 - [i18]Yufan Wang, Kai Ming Ting, Yuanyi Shang:
A principled distributional approach to trajectory similarity measurement. CoRR abs/2301.00393 (2023) - [i17]Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song:
Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. CoRR abs/2301.06794 (2023) - [i16]Zijing Wang, Ye Zhu, Kai Ming Ting:
Distribution-Based Trajectory Clustering. CoRR abs/2310.05123 (2023) - 2022
- [j47]Ye Zhu, Kai Ming Ting, Yuan Jin, Maia Angelova:
Hierarchical clustering that takes advantage of both density-peak and density-connectivity. Inf. Syst. 103: 101871 (2022) - [j46]Kai Ming Ting, Zongyou Liu, Hang Zhang, Ye Zhu:
A New Distributional Treatment for Time Series and An Anomaly Detection Investigation. Proc. VLDB Endow. 15(11): 2321-2333 (2022) - [j45]Xiangyu Song, Sunil Aryal, Kai Ming Ting, Zhen Liu, Bin He:
Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest. IEEE Trans. Geosci. Remote. Sens. 60: 1-16 (2022) - [j44]Ming Pang, Kai Ming Ting, Peng Zhao, Zhi-Hua Zhou:
Improving Deep Forest by Screening. IEEE Trans. Knowl. Data Eng. 34(9): 4298-4312 (2022) - [c74]Ye Zhu, Kai Ming Ting:
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract). IJCAI 2022: 5792-5796 - [c73]Xin Han, Ye Zhu, Kai Ming Ting, De-Chuan Zhan, Gang Li:
Streaming Hierarchical Clustering Based on Point-Set Kernel. KDD 2022: 525-533 - [i15]Yang Cao, Ye Zhu, Kai Ming Ting, Flora D. Salim, Hong Xian Li, Gang Li:
Detecting Change Intervals with Isolation Distributional Kernel. CoRR abs/2212.14630 (2022) - 2021
- [j43]Kai Ming Ting, Jonathan R. Wells, Takashi Washio:
Isolation kernel: the X factor in efficient and effective large scale online kernel learning. Data Min. Knowl. Discov. 35(6): 2282-2312 (2021) - [j42]Ye Zhu, Kai Ming Ting:
Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel. J. Artif. Intell. Res. 71: 667-695 (2021) - [j41]Ye Zhu, Kai Ming Ting, Mark J. Carman, Maia Angelova:
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern Recognit. 117: 107977 (2021) - [c72]Bi-Cun Xu, Kai Ming Ting, Yuan Jiang:
Isolation Graph Kernel. AAAI 2021: 10487-10495 - [c71]Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang:
Isolation Kernel Density Estimation. ICDM 2021: 619-628 - [c70]Yi-Xuan Xu, Ming Pang, Ji Feng, Kai Ming Ting, Yuan Jiang, Zhi-Hua Zhou:
Reconstruction-based Anomaly Detection with Completely Random Forest. SDM 2021: 127-135 - [i14]Kai Ming Ting, Takashi Washio, Ye Zhu, Yang Xu:
Breaking the curse of dimensionality with Isolation Kernel. CoRR abs/2109.14198 (2021) - 2020
- [j40]Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari:
A comparative study of data-dependent approaches without learning in measuring similarities of data objects. Data Min. Knowl. Discov. 34(1): 124-162 (2020) - [j39]Jonathan R. Wells, Sunil Aryal, Kai Ming Ting:
Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning. Knowl. Inf. Syst. 62(8): 3203-3216 (2020) - [c69]Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou:
Isolation Distributional Kernel: A New Tool for Kernel based Anomaly Detection. KDD 2020: 198-206 - [c68]Bo Chen, Kai Ming Ting, Tat-Jun Chin:
Anomaly Detection via Neighbourhood Contrast. PAKDD (2) 2020: 647-659 - [c67]Durgesh Samariya, Sunil Aryal, Kai Ming Ting, Jiangang Ma:
A New Effective and Efficient Measure for Outlying Aspect Mining. WISE (2) 2020: 463-474 - [i13]Kai Ming Ting, Jonathan R. Wells, Ye Zhu:
Clustering based on Point-Set Kernel. CoRR abs/2002.05815 (2020) - [i12]Kai Ming Ting, Bi-Cun Xu, Takashi Washio, Zhi-Hua Zhou:
Isolation Distributional Kernel: A New Tool for Point & Group Anomaly Detection. CoRR abs/2009.12196 (2020) - [i11]Xin Han, Ye Zhu, Kai Ming Ting, Gang Li:
The Impact of Isolation Kernel on Agglomerative Hierarchical Clustering Algorithms. CoRR abs/2010.05473 (2020)
2010 – 2019
- 2019
- [j38]Kai Ming Ting, Ye Zhu, Mark J. Carman, Yue Zhu, Takashi Washio, Zhi-Hua Zhou:
Lowest probability mass neighbour algorithms: relaxing the metric constraint in distance-based neighbourhood algorithms. Mach. Learn. 108(2): 331-376 (2019) - [j37]Jonathan R. Wells, Kai Ming Ting:
A new simple and efficient density estimator that enables fast systematic search. Pattern Recognit. Lett. 122: 92-98 (2019) - [c66]Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent C. S. Lee:
Nearest-Neighbour-Induced Isolation Similarity and Its Impact on Density-Based Clustering. AAAI 2019: 4755-4762 - [c65]Xin-Qiang Cai, Peng Zhao, Kai-Ming Ting, Xin Mu, Yuan Jiang:
Nearest Neighbor Ensembles: An Effective Method for Difficult Problems in Streaming Classification with Emerging New Classes. ICDM 2019: 970-975 - [c64]Bi-Cun Xu, Kai Ming Ting, Zhi-Hua Zhou:
Isolation Set-Kernel and Its Application to Multi-Instance Learning. KDD 2019: 941-949 - [i10]Sunil Aryal, Kai Ming Ting, Takashi Washio, Gholamreza Haffari:
A new simple and effective measure for bag-of-word inter-document similarity measurement. CoRR abs/1902.03402 (2019) - [i9]Ye Zhu, Kai Ming Ting:
Improving Stochastic Neighbour Embedding fundamentally with a well-defined data-dependent kernel. CoRR abs/1906.09744 (2019) - [i8]Xiaoyu Qin, Kai Ming Ting, Ye Zhu, Vincent C. S. Lee:
Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering. CoRR abs/1907.00378 (2019) - [i7]Kai Ming Ting, Jonathan R. Wells, Takashi Washio:
Isolation Kernel: The X Factor in Efficient and Effective Large Scale Online Kernel Learning. CoRR abs/1907.01104 (2019) - 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. Comput. Intell. 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. Mach. Learn. 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 Recognit. 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) - [c63]Kai Ming Ting, Sunil Aryal, Takashi Washio:
Which Outlier Detector Should I use? ICDM 2018: 8 - [c62]Ming Pang, Kai-Ming Ting, Peng Zhao, Zhi-Hua Zhou:
Improving Deep Forest by Confidence Screening. ICDM 2018: 1194-1199 - [c61]Kai Ming Ting, Yue Zhu, Zhi-Hua Zhou:
Isolation Kernel and Its Effect on SVM. KDD 2018: 2329-2337 - [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 - [i6]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) - [i5]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) - [i4]Kai Ming Ting, Takashi Washio, Ata Kabán:
Data Dependent Dissimilarity Measures (NII Shonan Meeting 2018-13). NII Shonan Meet. Rep. 2018 (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. Mach. Learn. 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]Kai Ming Ting:
Confusion Matrix. Encyclopedia of Machine Learning and Data Mining 2017: 260 - [r9]Kai Ming Ting:
Error Rate. Encyclopedia of Machine Learning and Data Mining 2017: 414 - [r8]Kai Ming Ting:
Precision. Encyclopedia of Machine Learning and Data Mining 2017: 990 - [r7]Kai Ming Ting:
Precision and Recall. Encyclopedia of Machine Learning and Data Mining 2017: 990-991 - [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. Comput. Intell. 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. Mach. Learn. 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 Recognit. 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. Mach. Learn. 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 Recognit. 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. Comput. Intell. 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. Mach. Learn. 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 Comput. Appl. 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 Networks Learn. 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. Mach. Learn. 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 Recognit. 45(4): 1707-1720 (2012) - [j16]Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou:
Isolation-Based Anomaly Detection. ACM Trans. Knowl. Discov. Data 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. Mach. Learn. 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 Recognit. 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 Recognit. Lett. 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. Multim. 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]Kai Ming Ting, Ian H. Witten:
Issues in Stacked Generalization. CoRR abs/1105.5466 (2011) - 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]Kai Ming Ting:
Confusion Matrix. Encyclopedia of Machine Learning 2010: 209 - [r4]Kai Ming Ting:
Error Rate. Encyclopedia of Machine Learning 2010: 331 - [r3]Kai Ming Ting:
Precision. Encyclopedia of Machine Learning 2010: 780 - [r2]Kai Ming Ting:
Precision and Recall. Encyclopedia of Machine Learning 2010: 781 - [r1]Kai Ming Ting:
Sensitivity and Specificity. Encyclopedia of Machine Learning 2010: 901-902
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]Kai Ming Ting, Lian Zhu:
Boosting Support Vector Machines Successfully. MCS 2009: 509-518 - 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]Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou:
Isolation Forest. ICDM 2008: 413-422 - [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. Mach. Learn. 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]Yang Yu, Zhi-Hua Zhou, Kai Ming Ting:
Cocktail Ensemble for Regression. ICDM 2007: 721-726 - 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]Fei Tony Liu, Kai Ming Ting:
Variable Randomness in Decision Tree Ensembles. PAKDD 2006: 81-90 - 2005
- [j7]Geoffrey I. Webb, Kai Ming Ting:
On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Mach. Learn. 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. Comput. Intell. 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]Kai Ming Ting:
Issues in Classifier Evaluation using Optimal Cost Curves. ICML 2002: 642-649 - 2000
- [c15]Kai Ming Ting:
An Empirical Study of MetaCost Using Boosting Algorithms. ECML 2000: 413-425 - [c14]Kai Ming Ting:
A Comparative Study of Cost-Sensitive Boosting Algorithms. ICML 2000: 983-990
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]Kai Ming Ting, Zijian Zheng:
Boosting Cost-Sensitive Trees. Discovery Science 1998: 244-255 - [c9]Kai Ming Ting, Zijian Zheng:
Boosting Trees for Cost-Sensitive Classifications. ECML 1998: 190-195 - [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]Kai Ming Ting:
Inducing Cost-Sensitive Trees via Instance Weighting. PKDD 1998: 139-147 - 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]Kai Ming Ting, Ian H. Witten:
Stacking Bagged and Dagged Models. ICML 1997: 367-375 - [c4]Kai Ming Ting, Ian H. Witten:
Stacked Generalizations: When Does It Work? IJCAI (2) 1997: 866-873 - 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
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