Misc - mlr-org/mlr GitHub Wiki

Publications that use mlr

This is a list of publications that we know use mlr.

  • Mersmann, Olaf, Bernd Bischl, Heike Trautmann, Mike Preuss, Claus Weihs, and Günter Rudolph. "Exploratory landscape analysis." In Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp. 829-836. ACM, 2011.

  • Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Claus Weihs. "Resampling methods for meta-model validation with recommendations for evolutionary computation." Evolutionary Computation 20, no. 2 (2012): 249-275.

  • Koch, Patrick, Bernd Bischl, Oliver Flasch, Thomas Bartz-Beielstein, Claus Weihs, and Wolfgang Konen. "Tuning and evolution of support vector kernels." Evolutionary Intelligence 5, no. 3 (2012): 153-170.

  • Bischl, Bernd, Pascal Kerschke, Lars Kotthoff, Marius Lindauer, Yuri Malitsky, Alexandre Fréchette, Holger Hoos et al. "Aslib: A benchmark library for algorithm selection." Artificial Intelligence 237 (2016): 41-58.

  • Konen, Wolfgang, Patrick Koch, Oliver Flasch, Thomas Bartz-Beielstein, Martina Friese, and Boris Naujoks. "Tuned data mining: A benchmark study on different tuners." In Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp. 1995-2002. ACM, 2011.

  • Bischl, Bernd, Olaf Mersmann, and Heike Trautmann. "Resampling methods in model validation." In Workshop on Experimental Methods for the Assessment of Computational Systems (WEMACS 2010), held in conjunction with the International Conference on Parallel Problem Solving From Nature (PPSN 2010), Krakow, Poland, Sept, vol. 11, p. 14. 2010.

  • Konen, Wolfgang, Patrick Koch, Oliver Flasch, and Thomas Bartz-Beielstein. "Parameter-tuned data mining: A general framework." In Proceedings, vol. 20. 2010.

  • Bischl, Bernd, Simon Wessing, Nadja Bauer, Klaus Friedrichs, and Claus Weihs. "MOI-MBO: Multiobjective infill for parallel model-based optimization." In International Conference on Learning and Intelligent Optimization, pp. 173-186. Springer International Publishing, 2014.

  • Krey, Sebastian, and Uwe Ligges. "SVM based instrument and timbre classification." In Classification as a Tool for Research, pp. 759-766. Springer Berlin Heidelberg, 2010.

  • Eugster, Manuel JA, Friedrich Leisch, and Carolin Strobl. "(Psycho-) analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithms." Computational Statistics & Data Analysis 71 (2014): 986-1000.

  • Blume, Holger, Bernd Bischl, Martin Botteck, Christian Igel, Rainer Martin, Günther Roetter, Günter Rudolph, Wolfgang Theimer, Igor Vatolkin, and Claus Weihs. "Huge music archives on mobile devices." IEEE Signal Processing Magazine 28, no. 4 (2011): 24-39.

  • Koch, Patrick, Thomas Bartz-Beielstein, and Wolfgang Konen. "Optimization of support vector regression models for stormwater prediction." InProceedings 20. workshop computational intelligence. Universitätsverlag Karlsruhe, http://www. gm. fh-koeln. de/konen/Publikationen/GMACI10_optimSVR. pdf. 2010.

  • Ligges, Uwe, and Sebastian Krey. "Feature clustering for instrument classification." Computational Statistics 26, no. 2 (2011): 279-291.

  • Krey, Sebastian, Uwe Ligges, and Friedrich Leisch. "Music and timbre segmentation by recursive constrained K-means clustering." Computational Statistics 29, no. 1-2 (2014): 37-50.

  • Szepannek, Gero, Matthias Gruhne, Bernd Bischl, Sebastian Krey, Tamas Harczos, Frank Klefenz, Christian Dittmar, and Claus Weihs. "Perceptually based phoneme recognition in popular music." In Classification as a Tool for Research, pp. 751-758. Springer Berlin Heidelberg, 2010.

  • Kerschke, Pascal, Mike Preuss, Carlos Hernández, Oliver Schütze, Jian-Qiao Sun, Christian Grimme, Günter Rudolph, Bernd Bischl, and Heike Trautmann. "Cell mapping techniques for exploratory landscape analysis." InEVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation V, pp. 115-131. Springer International Publishing, 2014.

  • Kerschke, Pascal, Mike Preuss, Simon Wessing, and Heike Trautmann. "Detecting Funnel Structures by Means of Exploratory Landscape Analysis." In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 265-272. ACM, 2015.

  • Bischl, Bernd, Julia Schiffner, and Claus Weihs. "Benchmarking local classification methods." Computational Statistics 28, no. 6 (2013): 2599-2619.

  • Lei, Tailong, Youyong Li, Yunlong Song, Dan Li, Huiyong Sun, and Tingjun Hou. "ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling."Journal of cheminformatics 8, no. 1 (2016): 1.

  • Kotthoff, Lars. "ICON Challenge on Algorithm Selection." arXiv preprint arXiv:1511.04326 (2015).

  • Schiffner, Julia, Bernd Bischl, and Claus Weihs. "Bias-variance analysis of local classification methods." In Challenges at the Interface of Data Analysis, Computer Science, and Optimization, pp. 49-57. Springer Berlin Heidelberg, 2012.

  • Kotthaus, Helena, Ingo Korb, and Peter Marwedel. "Performance Analysis for Parallel R Programs: Towards Efficient Resource Utilization." Abstract Booklet of the International R User Conference (UseR.

  • Schiffner, Julia, Erhard Godehardt, Stefanie Hillebrand, Alexander Albert, Artur Lichtenberg, and Claus Weihs. "Identification of Risk Factors in Coronary Bypass Surgery." In Algorithms from and for Nature and Life, pp. 287-295. Springer International Publishing, 2013.

  • Mejri, Dhouha, Mohamed Limam, and Claus Weihs. "Monitoring a Dynamic Weighted Majority Dynamic weighted majority Method Based on Datasets with Concept Drift." In Analysis of Large and Complex Data, pp. 241-250. Springer International Publishing, 2016.

  • Koch, Patrick, Wolfgang Konen, Oliver Flasch, Martina Friese, Boris Naujoks, Martin Zaefferer, and Thomas Bartz-Beielstein. "Tuned Data Mining in R." Hüllermeier, E.(Hrsg.): Proceedings 21 (2011): 147-160.

  • Probst, Philipp. "Anwendung von Multilabel-Klassifikationsverfahren auf Medizingerätestatusreporte zur Generierung von Reparaturvorschlägen."

  • Friedrichs, K., 2016. Musikklassifikation mittels auditorischer Modelle zur Optimierung von Hörgeräten (Doctoral dissertation).

mlr developer team and contributors page

This page list the mlr developers over time, and what other individual have contributed to the project. Please add yourself here when you feel you have done something worthwhile.

Github Avatar Bernd Bischl

I am professor of computational statistics at the LMU Munich. I created mlr a long time ago at the beginning of my PhD. I have worked on many different parts of mlr, mainly the internal OO system, the wrappers and tuning - although quite a few people helped to refactor the package a lot and to turn it into something much better. I have probably contributed next to nothing to the great tutorial.

Github Avatar Michel Lang

I am a postdoc at the TU Dortmund and one of the main developers of mlr. I've worked on many internal parts of mlr and started to implement support for survival analysis.

Github Avatar Lars Kotthoff

I am assistant professor of Computer Science at the University of Wyoming. My main contributions to mlr include support for clustering. Apart from that I'm usually fighting Travis in one way or another.

Github Avatar Jakob Richter

PhD Student in Statistics at TU Dortmund. Working on mlr since 2012. Always wanting to add some functionality, ending up revising a lot of stuff. Also involved in the development in related packages as ParamHelpers and mainly mlrMBO recently.

Github Avatar Giuseppe Casalicchio

I am a PhD student at the LMU Munich and member of the computational statistics working group. I added support for several stacking algorithms.

Github Avatar Zachary Jones

I am a PhD student at Pennsylvania State University and a former Google Summer of Code student. I work mainly on visualization, variance estimation for predictions, and functionality for exploratory data analysis. I am the plot master.

Github Avatar Erich Studerus

I am postdoc psychologist at the University of Basel Psychiatrics Clinics. I added support for several learners and filtering methods.

Github Avatar Julia Schiffner

I am a researcher at Heinrich Heine University Düsseldorf. I work mainly on expanding and improving the tutorial, but also do nice things for mlr itself.

Github Avatar Florian Fendt

I am a Master's student at the LMU Munich and member of the computational statistics working group. I'm helping to clean up the issue tracker in general and will be implementing time series tasks in the course of my Master's Thesis.

Github Avatar Florian Pfisterer

Master's Student at LMU Munich, implemented some visualizations on BenchmarkResults and hopefully some more in the future.

Github Avatar Philipp Probst

PhD Student at IBE, LMU Munich. Implemented (parts of) the multilabel classification in mlr. Currently doing benchmarks on OpenML datasets with mlr, comparing different learners and getting informations and good defaults for hyperparameters of implemented learners.

Github Avatar Janek Thomas

PhD Student at LMU Munich and member of the computational statistics working group. I'm interested in variable selection and hyperparameter tuning, especially for gradient boosting. I work on variable importance, tuning and preprocessing wrappers.

Github Avatar Bruno Hebling Vieira

MSc in Physics Applied to Medicine and Biology and BSc in Medical Physics, currently pursuing a DSc also from the University of São Paulo (USP). I'm committed to add new useful measures and learners to mlr.

Github Avatar Mason Gallo

I am a graduate student at Georgia Tech with industry experience in machine learning. I implemented hyperparameter tuning visualization, and I also work on various parts of mlr along with the tutorial.

Github Avatar Quay Au

I am a PhD student at LMU Munich and member of the computational statistics working group. I implemented several multilabel algorithms.

Julia Fried

I am studying Data Science at the LMU Munich. I've created the mlr cheatsheet and added use cases to the mlr tutorial.

Github Avatar Kira Engelhardt

I am a Data Science student at LMU Munich. I designed the Cheatsheet and worked on the tutorial.

Github Avatar Patrick Schratz

PhD Student at Friedrich-Schiller-University Jena. Environmental modeling with a focus on spatial data handling. I implemented the possibility to use spatially disjoint subsets in cross-validation settings including the corresponding tutorial section "Handling of Spatial Data".

NEWS

mlr 2.14

learners -- new

  • {classif,regr}.liquidSVM

mlr 2.13

general

  • Stratification can happen on integer columns now. Only doubles are not allowed.
  • Get the inner indices of a (nested) resampling setting that was called using resample(extract = getTuneResult).

functions - new

  • getResamplingIndices() to get nested resampling indices for tuning and feature selection.

learners -- general

  • regr.ranger now supports instance weights.

measures -- general

  • BAC now supports multiclass.