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ORCIDJonathan R. Wells
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2020 – today
- 2024
[i4]Sunil Aryal, Jonathan R. Wells, Arbind Agrahari Baniya
, KC Santosh:
Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing. CoRR abs/2401.11402 (2024)- 2023
- [j11]Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang, Ye Zhu:
Isolation Kernel Estimators. Knowl. Inf. Syst. 65(2): 759-787 (2023) - [j10]Kai Ming Ting, Jonathan R. Wells, Ye Zhu:
Point-Set Kernel Clustering. IEEE Trans. Knowl. Data Eng. 35(5): 5147-5158 (2023) - 2021
- [j9]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) - [c8]Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang:
Isolation Kernel Density Estimation. ICDM 2021: 619-628 - [c7]Sunil Aryal, Jonathan R. Wells:
Ensemble of Local Decision Trees for Anomaly Detection in Mixed Data. ECML/PKDD (1) 2021: 687-702 - 2020
- [j8]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) - [i3]Kai Ming Ting, Jonathan R. Wells, Ye Zhu:
Clustering based on Point-Set Kernel. CoRR abs/2002.05815 (2020)
2010 – 2019
- 2019
- [j7]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) - [i2]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
- [j6]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) - 2017
- [j5]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) - [i1]Jonathan R. Wells, Kai Ming Ting:
A simple efficient density estimator that enables fast systematic search. CoRR abs/1707.00783 (2017) - 2014
- [j4]Jonathan R. Wells, Kai Ming Ting, Takashi Washio:
LiNearN: A new approach to nearest neighbour density estimator. Pattern Recognit. 47(8): 2702-2720 (2014) - [c6]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 - [c5]Sunil Aryal, Kai Ming Ting, Jonathan R. Wells, Takashi Washio:
Improving iForest with Relative Mass. PAKDD (2) 2014: 510-521 - 2013
- [j3]Kai Ming Ting, Lian Zhu, Jonathan R. Wells:
Local Models - the Key to Boosting Stable Learners Successfully. Comput. Intell. 29(2): 331-356 (2013) - [j2]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) - 2012
- [c4]Jonathan R. Wells, Kai Ming Ting, Naiwala P. Chandrasiri:
A non-time series approach to vehicle related time series problems. AusDM 2012: 61-70 - 2011
- [j1]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) - [c3]Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu:
Density Estimation Based on Mass. ICDM 2011: 715-724 - 2010
- [c2]Kai Ming Ting, Jonathan R. Wells:
Multi-dimensional Mass Estimation and Mass-based Clustering. ICDM 2010: 511-520
2000 – 2009
- 2009
- [c1]Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb:
FaSS: Ensembles for Stable Learners. MCS 2009: 364-374
Coauthor Index
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