Jonathan R. Wells

According to our database, Jonathan R. Wells authored at least 22 papers between 2009 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Enabling clustering algorithms to detect clusters of varying densities through scale-invariant data preprocessing.

Sunil Aryal, Jonathan R. Wells, Arbind Agrahari Baniya, KC Santosh

CoRR, 2024

2023

Point-Set Kernel Clustering.

Kai Ming Ting, Jonathan R. Wells, Ye Zhu

IEEE Trans. Knowl. Data Eng., May, 2023

Isolation Kernel Estimators.

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang, Ye Zhu

Knowl. Inf. Syst., February, 2023

2021

Isolation kernel: the X factor in efficient and effective large scale online kernel learning.

Kai Ming Ting, Jonathan R. Wells, Takashi Washio

Data Min. Knowl. Discov., 2021

Ensemble of Local Decision Trees for Anomaly Detection in Mixed Data.

Sunil Aryal, Jonathan R. Wells

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Isolation Kernel Density Estimation.

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Hang Zhang

Proceedings of the IEEE International Conference on Data Mining, 2021

2020

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

Jonathan R. Wells, Sunil Aryal, Kai Ming Ting

Knowl. Inf. Syst., 2020

Clustering based on Point-Set Kernel.

Kai Ming Ting, Jonathan R. Wells, Ye Zhu

CoRR, 2020

2019

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

Jonathan R. Wells, Kai Ming Ting

Pattern Recognit. Lett., 2019

2018

Isolation-based anomaly detection using nearest-neighbor ensembles.

Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Ye Zhu, Jonathan R. Wells

Comput. Intell., 2018

2017

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

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Sunil Aryal

Mach. Learn., 2017

A simple efficient density estimator that enables fast systematic search.

Jonathan R. Wells, Kai Ming Ting

CoRR, 2017

2014

LiNearN: A new approach to nearest neighbour density estimator.

Jonathan R. Wells, Kai Ming Ting, Takashi Washio

Pattern Recognit., 2014

Improving iForest with Relative Mass.

Sunil Aryal, Kai Ming Ting, Jonathan R. Wells, Takashi Washio

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

Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble.

Tharindu R. Bandaragoda, Kai Ming Ting, David W. Albrecht, Fei Tony Liu, Jonathan R. Wells

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

2013

DEMass: a new density estimator for big data.

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu, Sunil Aryal

Knowl. Inf. Syst., 2013

Local Models - the Key to Boosting Stable Learners Successfully.

Kai Ming Ting, Lian Zhu, Jonathan R. Wells

Comput. Intell., 2013

2012

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

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

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

2011

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

Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb

Mach. Learn., 2011

Density Estimation Based on Mass.

Kai Ming Ting, Takashi Washio, Jonathan R. Wells, Fei Tony Liu

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

2010

Multi-dimensional Mass Estimation and Mass-based Clustering.

Kai Ming Ting, Jonathan R. Wells

Proceedings of the ICDM 2010, 2010

2009

FaSS: Ensembles for Stable Learners.

Kai Ming Ting, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, Geoffrey I. Webb

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