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.
Theoretical and Empirical Analysis of ReliefF and RReliefF
20032.2k citationsMarko Robnik‐Šikonja, Igor Kononenkoprofile →
Explaining prediction models and individual predictions with feature contributions
20131.3k citationsErik Štrumbelj, Igor Kononenkoprofile →
Machine learning for medical diagnosis: history, state of the art and perspective
Countries citing papers authored by Igor Kononenko
Since
Specialization
Citations
This map shows the geographic impact of Igor Kononenko'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 Igor Kononenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Igor Kononenko more than expected).
This network shows the impact of papers produced by Igor Kononenko. 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 Igor Kononenko. The network helps show where Igor Kononenko may publish in the future.
Co-authorship network of co-authors of Igor Kononenko
This figure shows the co-authorship network connecting the top 25 collaborators of Igor Kononenko.
A scholar is included among the top collaborators of Igor Kononenko 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 Igor Kononenko. Igor Kononenko is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Bosnić, Zoran & Igor Kononenko. (2010). Correction of Regression Predictions Using the Secondary Learner on the Sensitivity Analysis Outputs. Computing and Informatics / Computers and Artificial Intelligence. 29(6). 929–946.3 indexed citations
9.
Štrumbelj, Erik & Igor Kononenko. (2010). An Efficient Explanation of Individual Classifications using Game Theory. Journal of Machine Learning Research. 11(1). 1–18.220 indexed citations
10.
Bosnić, Zoran & Igor Kononenko. (2008). Estimation of Regressor Reliability. Journal of Intelligent Systems. 17(1-3). 297–311.3 indexed citations
Robnik‐Šikonja, Marko & Igor Kononenko. (2001). Comprehensible Interpretation of Relief's Estimates. International Conference on Machine Learning. 433–440.15 indexed citations
Robnik‐Šikonja, Marko & Igor Kononenko. (1999). Attribute Dependencies, Understandability and Split Selection in Tree Based Models. International Conference on Machine Learning. 344–353.7 indexed citations
15.
Kukar, Matjaž & Igor Kononenko. (1998). Cost-Sensitive Learning with Neural Networks.. European Conference on Artificial Intelligence. 445–449.158 indexed citations
16.
Robnik‐Šikonja, Marko & Igor Kononenko. (1998). Pruning Regression Trees with MDL.. European Conference on Artificial Intelligence. 455–459.16 indexed citations
17.
Lavrač, Nada, Igor Kononenko, Elpida Keravnou, Matjaž Kukar, & Blaž Zupan. (1998). Intelligent data analysis for medical diagnosisc using machine learning and temporal abstraction. AI Communications. 11(3). 191–218.17 indexed citations
18.
Robnik‐Šikonja, Marko & Igor Kononenko. (1997). An adaptation of Relief for attribute estimation in regression. International Conference on Machine Learning. 296–304.335 indexed citations
19.
Kononenko, Igor. (1995). On biases in estimating multi-valued attributes. International Joint Conference on Artificial Intelligence. 1034–1040.157 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.