Ronny Kohavi
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Ronny Kohavicomputer-science Degrees
Computer Science
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Data Mining
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Machine Learning
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Computer Science
Ronny Kohavi's Degrees
- PhD Computer Science Stanford University
- Masters Computer Science Stanford University
- Bachelors Computer Science Stanford University
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Why Is Ronny Kohavi Influential?
(Suggest an Edit or Addition)Ronny Kohavi's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection (1995) (11917)
- Wrappers for Feature Subset Selection (1997) (8630)
- Irrelevant Features and the Subset Selection Problem (1994) (2767)
- An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants (1999) (2592)
- Supervised and Unsupervised Discretization of Continuous Features (1995) (2219)
- Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid (1996) (1708)
- The Case against Accuracy Estimation for Comparing Induction Algorithms (1998) (1224)
- Feature Selection for Knowledge Discovery and Data Mining (1998) (1125)
- The Power of Decision Tables (1995) (859)
- Bias Plus Variance Decomposition for Zero-One Loss Functions (1996) (759)
- Controlled experiments on the web: survey and practical guide (2009) (662)
- Real world performance of association rule algorithms (2001) (535)
- Data Mining Using MLC a Machine Learning Library in C++ (1996) (413)
- Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology (1995) (390)
- Wrappers for feature selection (1997) (373)
- Wrappers for performance enhancement and oblivious decision graphs (1995) (362)
- Practical guide to controlled experiments on the web: listen to your customers not to the hippo (2007) (356)
- Online controlled experiments at large scale (2013) (336)
- Error-Based and Entropy-Based Discretization of Continuous Features (1996) (327)
- Emerging trends in business analytics (2002) (316)
- KDD-Cup 2000 organizers' report: peeling the onion (2000) (308)
- Lazy Decision Trees (1996) (302)
- Guest Editors' Introduction: On Applied Research in Machine Learning (1998) (294)
- MLC++: a machine learning library in C++ (1994) (227)
- Trustworthy online controlled experiments: five puzzling outcomes explained (2012) (223)
- Pruning Decision Trees with Misclassification Costs (1998) (222)
- The Wrapper Approach (1998) (211)
- Automatic Parameter Selection by Minimizing Estimated Error (1995) (200)
- Mining e-commerce data: the good, the bad, and the ugly (2001) (184)
- Seven rules of thumb for web site experimenters (2014) (181)
- Improving the sensitivity of online controlled experiments by utilizing pre-experiment data (2013) (178)
- Online Controlled Experiments and A/B Testing (2017) (169)
- Improving simple Bayes (1997) (168)
- Lessons and Challenges from Mining Retail E-Commerce Data (2004) (153)
- Data mining tasks and methods: Classification: decision-tree discovery (2002) (153)
- MineSet: An Integrated System for Data Mining (1997) (140)
- Online Experiments: Lessons Learned (2007) (139)
- Option Decision Trees with Majority Votes (1997) (134)
- Integrating e-commerce and data mining: architecture and challenges (2000) (134)
- Applications of Data Mining to Electronic Commerce (2000) (132)
- Decision tree discovery (1999) (125)
- The Utility of Feature Weighting in Nearest-Neighbor Algorithms (1997) (124)
- Web Mining (2004) (113)
- Seven pitfalls to avoid when running controlled experiments on the web (2009) (108)
- Online Experimentation at Microsoft (2009) (103)
- Bottom-Up Induction of Oblivious Read-Once Decision Graphs: Strengths and Limitations (1994) (97)
- Useful Feature Subsets and Rough Set Reducts (1994) (95)
- Top Challenges from the first Practical Online Controlled Experiments Summit (2019) (88)
- Feature Subset Selection as Search with Probabilistic Estimates (1994) (85)
- Trustworthy Online Controlled Experiments (2020) (84)
- Oblivious Decision Trees, Graphs, and Top-Down Pruning (1995) (83)
- Ten Supplementary Analyses to Improve E-commerce Web Sites (2003) (82)
- The Surprising Power of Online Experiments (2017) (80)
- Unexpected results in online controlled experiments (2011) (77)
- Targeting Business Users with Decision Table Classifiers (1998) (69)
- Applications of Data Mining to Electronic Commerce (2000) (68)
- Online Controlled Experiments and A / B Tests (2015) (68)
- Data Mining and Visualization (2000) (63)
- Visualizing the Simple Bayesian Classi er (1997) (63)
- Pitfalls of long-term online controlled experiments (2016) (59)
- Online Experiments: Practical Lessons (2010) (48)
- KDD-2004 : proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 22-25, 2004, Seattle, Washington, USA (2004) (44)
- WEBKDD 2001 — Mining Web Log Data Across All Customers Touch Points (2002) (41)
- Data Mining using MLC (1996) (33)
- Visualizing the simple Baysian classifier (2001) (31)
- Data mining using /spl Mscr//spl Lscr//spl Cscr/++ a machine learning library in C++ (1996) (29)
- Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (2004) (25)
- Online Controlled Experiments: Lessons from Running A/B/n Tests for 12 Years (2015) (23)
- IN BUSINESS ANALYTICS (2002) (21)
- Visualizing RFM Segmentation (2004) (21)
- Special Issue on Applications of data mining to electronic commerce (2001) (14)
- Online randomized controlled experiments at scale: lessons and extensions to medicine (2020) (14)
- A/B Testing at Scale: Accelerating Software Innovation (2017) (13)
- Online controlled experiments: introduction, learnings, and humbling statistics (2012) (11)
- Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points (2001) (9)
- WEBKDD 2000 - Web Mining for E-Commerce (2000) (7)
- Integrating E-Commerce and Data Mining (2000) (6)
- Data mining tasks and methods: Classification: Bayesian classification (2002) (6)
- Focus the Mining Beacon: Lessons and Challenges from the World of E-Commerce (2005) (5)
- Data Mining with MineSet: What Worked, What Did Not, and What Might (1998) (5)
- Online controlled experiments: introduction, insights, scaling, and humbling statistics (2013) (5)
- A/B Testing at Scale: Accelerating Software Innovation (2019) (4)
- KDD-99 panel report: data mining into vertical solutions (2000) (4)
- A Third Dimension to Rough Sets (2007) (3)
- Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology (2022) (3)
- A/B Testing Intuition Busters: Common Misunderstandings in Online Controlled Experiments (2022) (3)
- Knowledge discovery and data mining cup part of SIGKDD 2000 (2000) (3)
- Research Note on Decision Lists (1993) (2)
- Empirical Methods for Artiicial Intelligence (2007) (2)
- JMIM: A Feature Selection Technique using Joint Mutual Information Maximization Approach (2020) (2)
- Book Review: Empirical Methods for Artificial Intelligence (1996) (1)
- Web mining for e-commerce (workshop session) (title only) (2000) (1)
- AIJ special issue on relevance Wrappers for Feature Subset Selection (1996) (1)
- 1 THE WRAPPER APPROACH (1997) (0)
- Case studies: Public domain, multiple mining tasks systems: MLC++ (2002) (0)
- Case studies: Commercial, multiple mining tasks systems: mineSet (2002) (0)
- Metrics for Experimentation and the Overall Evaluation Criterion (2020) (0)
- Ethics in Controlled Experiments (2020) (0)
- A Simple Method for Subset Selection Which Assists Learning Schemes in the Execution Of (1995) (0)
- Triggering for Improved Sensitivity (2020) (0)
- C 5 . 1 . 5 Bayesian Classi cation (1999) (0)
- Research note on decision lists (2004) (0)
- Complementary and Alternative Techniques to Controlled Experiments (2020) (0)
- Choosing a Randomization Unit (2020) (0)
- Sample Ratio Mismatch and Other Trust-Related Guardrail Metrics (2020) (0)
- Online Controlled Experiments at Large Scale in Society 5.0 (2019) (0)
- To appear in the Third International Workshop on Rough Sets and Soft Computing (RSSC 94) Useful Feature Subsets and Rough Set Reducts (2015) (0)
- Introductory Topics for Everyone (2020) (0)
- Variance Estimation and Improved Sensitivity: Pitfalls and Solutions (2020) (0)
- B o t t o m-U p Induction of Oblivious Read-Once Decision Graphs (2005) (0)
- Assessment 978-1108-72426-5 — Trustworthy Online Controlled Experiments (0)
- The A/A Test (2020) (0)
- Twyman’s Law and Experimentation Trustworthiness (2020) (0)
- Selected Topics for Everyone (2020) (0)
- Sgimlc ++ Utilities 2.0 (1996) (0)
- Client-Side Experiments (2020) (0)
- Running and Analyzing Experiments (2020) (0)
- Session details: Industry/govt track a5: computational advertising (2012) (0)
- To appear in the Third International Workshop on Rough Sets and Soft Computing (RSSC 94) A Third Dimension to Rough Sets (2015) (0)
- Appears in Ecml-98 as a Research Note a Longer Version Is Available as Ece Tr 98-3, Purdue University Pruning Decision Trees with Misclassiication Costs 1 Pruning Decision Trees (2008) (0)
- D2.1.2 Mlc ++ D2.1.2.1 Motivation for Mlc ++ (1998) (0)
- Measuring Long-Term Treatment Effects (2020) (0)
- The Statistics behind Online Controlled Experiments (2020) (0)
- Advanced Topics for Analyzing Experiments (2020) (0)
- Final Technical Report for Hybrid Algorithms and Oblivious Decision Graphs Using MLC (1996) (0)
- Observational Causal Studies (2020) (0)
- Appears in ECML-98 as a research note Pruning Decision Trees with Misclassi cation Costs (2015) (0)
- Experimentation Platform and Culture (2020) (0)
- Institutional Memory and Meta-Analysis (2020) (0)
- Advanced Topics for Building an Experimentation Platform (2020) (0)
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