Ken-ichi Matsumoto

Information Systems · 2016–2018

A researcher in the Science corpus: 4 papers; trusts 70 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Information Systems — 102 citations into it; #4,020 of 8,771 by trust here, #4,188 by citations
• Software — 89 citations into it; #557 of 1,227 by trust here, #512 by citations
• Artificial Intelligence — 47 citations into it
• Computer Networks and Communications — 34 citations into it; #7,419 of 9,886 by trust here, #7,492 by citations
• Statistics and Probability — 25 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Analytical Chemistry — 9 citations into it
• Molecular Biology — 8 citations into it

Whom Ken-ichi Matsumoto trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Max Kuhn
2. Ewout W. Steyerberg
3. Kaspar Rufibach
4. Martin J. Shepperd
5. Marco D'Ambros
6. Shane McIntosh
7. Chakkrit Kla Tantithamthavorn
8. Ahmed E. Hassan
9. Frauke Günther
10. Baljinder Ghotra

Who trusts Ken-ichi Matsumoto overall

1. Chakkrit Kla Tantithamthavorn
2. Ahmed E. Hassan

Papers from Ken-ichi Matsumoto's perspective

Ordered by how strongly the researchers Ken-ichi Matsumoto trusts endorse each paper. Same corpus, a different order for every researcher.

1. Random Forests
2. The Elements of Statistical Learning
3. SMOTE: Synthetic Minority Over-sampling Technique
4. Fair and balanced?: bias in bug-fix datasets (ESEC/SIGSOFT FSE)
5. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings (IEEE Transactions on Software Engineering)
6. Reliability and validity in comparative studies of software prediction models (IEEE Transactions on Software Engineering)
7. Predicting defects using network analysis on dependency graphs (2008 ACM/IEEE 30th International Conference on Software Engineering)
8. Scikit-learn: Machine Learning in Python
9. A Systematic Literature Review on Fault Prediction Performance in Software Engineering (IEEE Transactions on Software Engineering)
10. Random Search for Hyper-Parameter Optimization

Their papers

• The Impact of Automated Parameter Optimization on Defect Prediction Models (2018) — 386 citations
• The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018) — 295 citations
• An Empirical Comparison of Model Validation Techniques for Defect Prediction Models (2017) — 512 citations
• Automated Parameter Optimization of Classification Techniques for Defect Prediction Models (2016) — 353 citations