John Hancock

3.6k total citations · 3 hit papers
50 papers, 2.0k citations indexed

About

John Hancock is a scholar working on Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, John Hancock has authored 50 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 18 papers in Computer Networks and Communications and 13 papers in Electrical and Electronic Engineering. Recurrent topics in John Hancock's work include Imbalanced Data Classification Techniques (28 papers), Machine Learning and Data Classification (15 papers) and Network Security and Intrusion Detection (14 papers). John Hancock is often cited by papers focused on Imbalanced Data Classification Techniques (28 papers), Machine Learning and Data Classification (15 papers) and Network Security and Intrusion Detection (14 papers). John Hancock collaborates with scholars based in United States, China and Switzerland. John Hancock's co-authors include Taghi M. Khoshgoftaar, Huanjing Wang, Qianxin Liang, Joffrey L. Leevy, Justin Johnson, Richard Zuech, Richard A. Bauder, Alice Leung, Tarek Abdelzaher and Dong Wang and has published in prestigious journals such as Journal Of Big Data, SN Computer Science and International Journal of Reliability Quality and Safety Engineering.

In The Last Decade

John Hancock

48 papers receiving 2.0k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

CatBoost for big data: an interdisciplinary review

2020 946 citations John Hancock, Taghi M. Khoshgoftaar Journal Of Big Data profile →
Survey on categorical data for neural networks

2020 334 citations John Hancock, Taghi M. Khoshgoftaar Journal Of Big Data profile →
Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods

2024 153 citations Huanjing Wang, Qianxin Liang et al. Journal Of Big Data profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
John Hancock United States	16	776	239	228	151	149	50	2.0k
Krishna Kant Singh India	27	582 0.8×	286 1.2×	298 1.3×	74 0.5×	162 1.1×	165	2.5k
Birmohan Singh India	17	591 0.8×	134 0.6×	161 0.7×	146 1.0×	118 0.8×	62	1.9k
Lisbeth Rodríguez-Mazahua Mexico	13	521 0.7×	216 0.9×	238 1.0×	118 0.8×	247 1.7×	48	2.1k
Asdrúbal López‐Chau Mexico	12	639 0.8×	137 0.6×	198 0.9×	109 0.7×	176 1.2×	61	2.1k
Yifan Shi China	12	771 1.0×	155 0.6×	171 0.8×	136 0.9×	107 0.7×	37	1.8k
Mamta Mittal India	28	1.0k 1.3×	269 1.1×	199 0.9×	155 1.0×	442 3.0×	109	3.2k
Wenming Cao China	14	1.1k 1.5×	127 0.5×	226 1.0×	124 0.8×	126 0.8×	40	2.1k
Md Ekrim Hossin Malaysia	5	681 0.9×	147 0.6×	119 0.5×	152 1.0×	182 1.2×	18	1.9k
Erwan Scornet France	11	861 1.1×	142 0.6×	235 1.0×	121 0.8×	180 1.2×	18	3.4k
Joaquin Vanschoren Netherlands	24	1.7k 2.2×	178 0.7×	195 0.9×	139 0.9×	272 1.8×	77	2.9k

Countries citing papers authored by John Hancock

Since Specialization

Citations

This map shows the geographic impact of John Hancock'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 John Hancock with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Hancock more than expected).

Fields of papers citing papers by John Hancock

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John Hancock. 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 John Hancock. The network helps show where John Hancock may publish in the future.

Co-authorship network of co-authors of John Hancock

This figure shows the co-authorship network connecting the top 25 collaborators of John Hancock. A scholar is included among the top collaborators of John Hancock 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 John Hancock. John Hancock is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Hancock, John, Taghi M. Khoshgoftaar, & Qianxin Liang. (2025). A problem-agnostic approach to feature selection and analysis using SHAP. Journal Of Big Data. 12(1). 13 indexed citations

Wang, Huanjing, Qianxin Liang, John Hancock, & Taghi M. Khoshgoftaar. (2024). Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods. Journal Of Big Data. 11(1). 153 indexed citations breakdown →

Hancock, John, Huanjing Wang, Taghi M. Khoshgoftaar, & Qianxin Liang. (2024). Data reduction techniques for highly imbalanced medicare Big Data. Journal Of Big Data. 11(1). 21 indexed citations

Wang, Huanjing, John Hancock, & Taghi M. Khoshgoftaar. (2024). Improving Credit Card Fraud Detection with Data Reduction Approaches. International Journal of Reliability Quality and Safety Engineering. 31(4).

Leevy, Joffrey L., et al.. (2023). Investigating the effectiveness of one-class and binary classification for fraud detection. Journal Of Big Data. 10(1). 7 indexed citations

Hancock, John, Richard A. Bauder, Huanjing Wang, & Taghi M. Khoshgoftaar. (2023). Explainable machine learning models for Medicare fraud detection. Journal Of Big Data. 10(1). 15 indexed citations

Leevy, Joffrey L., John Hancock, & Taghi M. Khoshgoftaar. (2023). Comparative analysis of binary and one-class classification techniques for credit card fraud data. Journal Of Big Data. 10(1). 17 indexed citations

Leevy, Joffrey L., et al.. (2023). One-Class Classifier Performance: Comparing Majority versus Minority Class Training. 86–91. 2 indexed citations

Hancock, John, Taghi M. Khoshgoftaar, & Justin Johnson. (2023). Using Area Under the Precision Recall Curve to Assess the Effect of Random Undersampling in the Classification of Imbalanced Medicare Big Data. International Journal of Reliability Quality and Safety Engineering. 31(1). 3 indexed citations

10.

Hancock, John & Taghi M. Khoshgoftaar. (2023). Exploring Maximum Tree Depth and Random Undersampling in Ensemble Trees to Optimize the Classification of Imbalanced Big Data. SN Computer Science. 4(5). 4 indexed citations

11.

Leevy, Joffrey L., Justin Johnson, John Hancock, & Taghi M. Khoshgoftaar. (2023). Threshold optimization and random undersampling for imbalanced credit card data. Journal Of Big Data. 10(1). 17 indexed citations

12.

Hancock, John & Taghi M. Khoshgoftaar. (2022). Hyperparameter Tuning for Medicare Fraud Detection in Big Data. SN Computer Science. 3(6). 5 indexed citations

13.

Zuech, Richard, John Hancock, & Taghi M. Khoshgoftaar. (2022). A new feature popularity framework for detecting cyberattacks using popular features. Journal Of Big Data. 9(1). 4 indexed citations

14.

Zuech, Richard, John Hancock, & Taghi M. Khoshgoftaar. (2021). Detecting SQL Injection Web Attacks Using Ensemble Learners and Data Sampling. 27–34. 7 indexed citations

15.

Zuech, Richard, John Hancock, & Taghi M. Khoshgoftaar. (2021). Investigating rarity in web attacks with ensemble learners. Journal Of Big Data. 8(1). 6 indexed citations

16.

Hancock, John & Taghi M. Khoshgoftaar. (2020). CatBoost for big data: an interdisciplinary review. Journal Of Big Data. 7(1). 94–94. 946 indexed citations breakdown →

17.

Hancock, John & Taghi M. Khoshgoftaar. (2020). Performance of CatBoost and XGBoost in Medicare Fraud Detection. 572–579. 40 indexed citations

18.

Wang, Shiguang, Tarek Abdelzaher, Sachin Kulkarni, et al.. (2014). Poster abstract: information-maximizing data collection in social sensing using named-data. Information Processing in Sensor Networks. 303–304. 1 indexed citations

19.

Wang, Shiguang, Tarek Abdelzaher, Sachin Kulkarni, et al.. (2014). The Information Funnel: Exploiting Named Data for Information-Maximizing Data Collection. 92–100. 11 indexed citations

20.

Huang, Hongzhao, Arkaitz Zubiaga, Heng Ji, et al.. (2012). Tweet Ranking Based on Heterogeneous Networks. International Conference on Computational Linguistics. 1239–1256. 20 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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