Jonathan R. Wells

458 total citations
16 papers, 259 citations indexed

About

Jonathan R. Wells is a scholar working on Artificial Intelligence, Signal Processing and Computer Networks and Communications. According to data from OpenAlex, Jonathan R. Wells has authored 16 papers receiving a total of 259 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 5 papers in Signal Processing and 4 papers in Computer Networks and Communications. Recurrent topics in Jonathan R. Wells's work include Anomaly Detection Techniques and Applications (11 papers), Time Series Analysis and Forecasting (4 papers) and Network Security and Intrusion Detection (4 papers). Jonathan R. Wells is often cited by papers focused on Anomaly Detection Techniques and Applications (11 papers), Time Series Analysis and Forecasting (4 papers) and Network Security and Intrusion Detection (4 papers). Jonathan R. Wells collaborates with scholars based in Australia, Japan and China. Jonathan R. Wells's co-authors include Kai Ming Ting, Fei Tony Liu, Tharindu Bandaragoda, David Albrecht, Ye Zhu, Takashi Washio, Sunil Aryal, Swee Chuan Tan, Shyh Wei Teng and Geoffrey I. Webb and has published in prestigious journals such as Pattern Recognition, IEEE Transactions on Knowledge and Data Engineering and Machine Learning.

In The Last Decade

Jonathan R. Wells

16 papers receiving 251 citations

Peers

Jonathan R. Wells

CNC

CSE

CVPR

Stefano Basta Italy

Maria Kontaki Greece

Houssam Zenati France

Bruno Lecouat France

Panpan Zheng China

Van Loi Cao Vietnam

Haowen Xu China

Michele Linardi France

Youjin Shin South Korea

Stefano Basta Italy

Jonathan R. Wells

240 ×1.1

106 ×1.0

CNC

75 ×1.0

35 ×1.3

CSE

32 ×1.1

CVPR

Citations per year, relative to Jonathan R. Wells Jonathan R. Wells (= 1×) peers Stefano Basta

Countries citing papers authored by Jonathan R. Wells

Since Specialization

Citations

This map shows the geographic impact of Jonathan R. Wells's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jonathan R. Wells with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jonathan R. Wells more than expected).

Fields of papers citing papers by Jonathan R. Wells

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jonathan R. Wells. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jonathan R. Wells. The network helps show where Jonathan R. Wells may publish in the future.

Co-authorship network of co-authors of Jonathan R. Wells

This figure shows the co-authorship network connecting the top 25 collaborators of Jonathan R. Wells. A scholar is included among the top collaborators of Jonathan R. Wells based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jonathan R. Wells. Jonathan R. Wells is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Ting, Kai Ming, et al.. (2022). Point-Set Kernel Clustering. IEEE Transactions on Knowledge and Data Engineering. 1–1. 8 indexed citations

Washio, Takashi, et al.. (2022). Isolation Kernel Estimators. Knowledge and Information Systems. 65(2). 759–787. 4 indexed citations

Ting, Kai Ming, Jonathan R. Wells, & Takashi Washio. (2021). Isolation kernel: the X factor in efficient and effective large scale online kernel learning. Data Mining and Knowledge Discovery. 35(6). 2282–2312. 4 indexed citations

Ting, Kai Ming, Takashi Washio, Jonathan R. Wells, & Hang Zhang. (2021). Isolation Kernel Density Estimation. 619–628. 2 indexed citations

Wells, Jonathan R., Sunil Aryal, & Kai Ming Ting. (2020). Simple supervised dissimilarity measure: Bolstering iForest-induced similarity with class information without learning. Knowledge and Information Systems. 62(8). 3203–3216. 2 indexed citations

Wright, John R., et al.. (2019). Intermediate Programming Methodologies for Manipulating Modern Humanoid Robots. 6(4). 214–222. 1 indexed citations

Wells, Jonathan R. & Kai Ming Ting. (2018). A new simple and efficient density estimator that enables fast systematic search. Pattern Recognition Letters. 122. 92–98. 5 indexed citations

Bandaragoda, Tharindu, Kai Ming Ting, David Albrecht, et al.. (2018). Isolation‐based anomaly detection using nearest‐neighbor ensembles. Computational Intelligence. 34(4). 968–998. 99 indexed citations

Ting, Kai Ming, Takashi Washio, Jonathan R. Wells, & Sunil Aryal. (2016). Defying the gravity of learning curve: a characteristic of nearest neighbour anomaly detectors. Machine Learning. 106(1). 55–91. 26 indexed citations

10.

Bandaragoda, Tharindu, Kai Ming Ting, David Albrecht, Fei Tony Liu, & Jonathan R. Wells. (2014). Efficient Anomaly Detection by Isolation Using Nearest Neighbour Ensemble. FedUni ResearchOnline (Federation University Australia). 698–705. 45 indexed citations

11.

Wells, Jonathan R., Kai Ming Ting, & Takashi Washio. (2014). LiNearN: A new approach to nearest neighbour density estimator. Pattern Recognition. 47(8). 2702–2720. 11 indexed citations

12.

Ting, Kai Ming, Takashi Washio, Jonathan R. Wells, Fei Tony Liu, & Sunil Aryal. (2013). DEMass: a new density estimator for big data. Knowledge and Information Systems. 35(3). 493–524. 8 indexed citations

13.

Wells, Jonathan R., et al.. (2012). A non-time series approach to vehicle related time series problems. FedUni ResearchOnline (Federation University Australia). 61–70. 2 indexed citations

14.

Ting, Kai Ming, et al.. (2012). LOCAL MODELS—THE KEY TO BOOSTING STABLE LEARNERS SUCCESSFULLY. Computational Intelligence. 29(2). 331–356. 1 indexed citations

15.

Ting, Kai Ming, Takashi Washio, Jonathan R. Wells, & Fei Tony Liu. (2011). Density Estimation Based on Mass. FedUni ResearchOnline (Federation University Australia). 715–724. 6 indexed citations

16.

Ting, Kai Ming, Jonathan R. Wells, Swee Chuan Tan, Shyh Wei Teng, & Geoffrey I. Webb. (2010). Feature-subspace aggregating: ensembles for stable and unstable learners. Machine Learning. 82(3). 375–397. 35 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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