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.
Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings
2008921 citationsBart Baesens, Christophe Mues et al.profile →
An experimental comparison of classification algorithms for imbalanced credit scoring data sets
Countries citing papers authored by Christophe Mues
Since
Specialization
Citations
This map shows the geographic impact of Christophe Mues'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 Christophe Mues with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christophe Mues more than expected).
This network shows the impact of papers produced by Christophe Mues. 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 Christophe Mues. The network helps show where Christophe Mues may publish in the future.
Co-authorship network of co-authors of Christophe Mues
This figure shows the co-authorship network connecting the top 25 collaborators of Christophe Mues.
A scholar is included among the top collaborators of Christophe Mues 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 Christophe Mues. Christophe Mues is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Mues, Christophe, et al.. (2011). Competing risks survival model for mortgage loans with simulated loss distributions. ePrints Soton (University of Southampton).2 indexed citations
5.
Tong, Edward, Christophe Mues, & Lyn C. Thomas. (2011). A zero-adjusted gamma model for estimating loss given default on residential mortgage loans. ePrints Soton (University of Southampton).3 indexed citations
6.
Mues, Christophe, et al.. (2010). Competing risks survival model for residential mortgage loans. ePrints Soton (University of Southampton).1 indexed citations
Brown, Iain, et al.. (2009). Benchmarking state-of-the-art regression algorithms for loss given default modelling. ePrints Soton (University of Southampton).1 indexed citations
Mues, Christophe, et al.. (2007). An empirical investigation into the interpretability of data mining models based on decision trees, tables and rules. ePrints Soton (University of Southampton).2 indexed citations
11.
Goedertier, Stijn, Christophe Mues, & Jan Vanthienen. (2007). Specifying Process-Aware Access Control Rules in SBVR, in Paschke, A. and Biletskiy, Y., editors, Advances in Rule Interchange and Applications. Lecture notes in computer science. 4824. 39–52.5 indexed citations
Setiono, Rudy, Christophe Mues, & Bart Baesens. (2006). Risk management and regulatory compliance: a data mining framework based on neural network rule extraction. Journal of the Association for Information Systems. 7.10 indexed citations
14.
Mues, Christophe & Jan Vanthienen. (2004). Improving the scalability of rule base verification using binary decision diagrams: An empirical study. Ki 2004 : advances in artificial intelligence. Proceedings. Lecture notes in computer science. 3238. 381–395.1 indexed citations
15.
Mues, Christophe, et al.. (2003). Knowledge discovery in data: van academische denkoefening naar bedrijfsrelevante praktijk. ePrints Soton (University of Southampton).
16.
Baesens, Bart, Christophe Mues, Rudy Setiono, Manu De Backer, & Jan Vanthienen. (2003). Building intelligent credit scoring systems using decision tables- Best paper nomination. International Conference on Enterprise Information Systems. 19–25.1 indexed citations
17.
Baesens, Bart, Rudy Setiono, Christophe Mues, Stijn Viaene, & Jan Vanthienen. (2001). Building Credit-Risk Evaluation Expert Systems Using Neural Network Rule Extraction and Decision Tables. Journal of the Association for Information Systems. 159–168.11 indexed citations
Vanthienen, Jan, et al.. (1995). A modelling approach to knowledge based systems verification. Document Server@UHasselt (UHasselt).1 indexed citations
20.
Vanthienen, Jan, et al.. (1995). A modularization approach to the verification of knowledge based systems. ePrints Soton (University of Southampton).1 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.