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[15]
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B. Bischl, M. Lang, O. Mersmann, J. Rahnenführer, and C. Weihs.
BatchJobs and BatchExperiments: Abstraction mechanisms for using
R in batch environments.
Journal of Statistical Software, 64(11):??–??, Mar. 2015.
[ bib |
http ]
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[14]
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G. Casalicchio, B. Bischl, A.-L. Boulesteix, and M. Schmid.
The residual-based predictiveness curve - a visual tool to assess the
performance of prediction models (PREPARING SUBMISSION).
Biometrics, 2015.
[ bib ]
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[13]
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M. Feilke, B. Bischl, V. Schmid, and J. Gertheiss.
Boosting in non-linear regression models with an application to
DCE-MRI data (PREPARING SUBMISSION).
Methods of Information in Medicine, 2015.
[ bib ]
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[12]
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D. Horn, A. Demircioğlu, B. Bischl, T. Glasmachers, and C. Weihs.
A comparative study on large scale kernelized support vector machines
(PREPARING SUBMISSION).
Advances in Data Analysis and Classification, 2015.
[ bib ]
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[11]
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H. Kotthaus, I. Korb, M. Lang, B. Bischl, J. Rahnenführer, and P. Marwedel.
Runtime and memory consumption analyses for machine learning R
programs.
Journal of Statistical Computation and Simulation,
85(1):14–29, 2015.
[ bib |
DOI |
arXiv |
.pdf |
http ]
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[10]
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M. Lang, H. Kotthaus, P. Marwedel, C. Weihs, J. Rahnenführer, and B. Bischl.
Automatic model selection for high-dimensional survival analysis.
Journal of Statistical Computation and Simulation,
85(1):62–76, 2015.
[ bib |
DOI |
.pdf |
http ]
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[9]
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J. Vanschoren, J. N. van Rijn, B. Bischl, and L. Torgo.
OpenML: Networked science in machine learning.
SIGKDD Explor. Newsl., 15(2):49–60, June 2014.
[ bib |
DOI |
.pdf |
http ]
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[8]
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B. Bischl, P. Kerschke, L. Kotthoff, M. Lindauer, Y. Malitsky,
A. Frechétte, H. Hoos, F. Hutter, K. Leyton-Brown, K. Tierney, and
J. Vanschoren.
ASlib: A benchmark library for algorithm selection (SUBMITTED).
Artificial Intelligence, 2014.
[ bib ]
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[7]
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O. Mersmann, M. Preuss, H. Trautmann, B. Bischl, and C. Weihs.
Analyzing the BBOB results by means of benchmarking concepts.
Evolutionary Computation Journal, 2014.
[ bib |
DOI |
.pdf |
http ]
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[6]
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B. Bischl, J. Schiffner, and C. Weihs.
Benchmarking local classification methods.
Computational Statistics, 28(6):2599–2619, 2013.
[ bib |
DOI |
.pdf |
http ]
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[5]
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O. Mersmann, B. Bischl, H. Trautmann, M. Wagner, J. Bossek, and F. Neumann.
A novel feature-based approach to characterize algorithm performance
for the traveling salesperson problem.
Annals of Mathematics and Artificial Intelligence, March:1–32,
2013.
[ bib |
DOI |
.pdf |
http ]
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[4]
|
B. Bischl, O. Mersmann, H. Trautmann, and C. Weihs.
Resampling methods for meta-model validation with recommendations for
evolutionary computation.
Evolutionary Computation, 20(2):249–275, 2012.
[ bib |
DOI |
http ]
|
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[3]
|
P. Koch, B. Bischl, O. Flasch, T. Bartz-Beielstein, C. Weihs, and W. Konen.
Tuning and evolution of support vector kernels.
Evolutionary Intelligence, 5(3):153–170, 2012.
[ bib |
DOI |
.pdf |
http ]
|
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[2]
|
H. Blume, B. Bischl, M. Botteck, C. Igel, R. Martin, G. Roetter, G. Rudolph,
W. Theimer, I. Vatolkin, and C. Weihs.
Huge music archives on mobile devices.
IEEE Signal Processing Magazine, 28(4):24–39, 2011.
[ bib |
DOI |
http ]
|
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[1]
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G. Szepannek, B. Bischl, and C. Weihs.
On the combination of locally optimal pairwise classifiers.
Journal of Engineering Applications of Artificial Intelligence,
22(1):79–85, 2008.
[ bib |
.pdf |
http ]
|
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[25]
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D. Brockhoff, B. Bischl, and T. Wagner.
The Impact of Initial Designs on the Performance of MATSuMoTo on the
Noiseless BBOB-2015 Testbed: A Preliminary Study.
In GECCO '15 Companion, Madrid, Spain, July 2015.
[ bib |
DOI |
http |
.pdf ]
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[24]
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N. Bauer, K. Friedrichs, B. Bischl, and C. Weihs.
Fast model based optimization of tone onset detection by instance
sampling (SUBMITTED).
In Data Analysis, Machine Learning and Knowledge Discovery,
Studies in Classification, Data Analysis, and Knowledge Organization, 2015.
[ bib ]
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[23]
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B. Bischl, T. Kühn, and G. Szepannek.
On class imbalancy correction for classification algorithms in credit
scoring (SUBMITTED).
In Operations Research Proceedings 2014, Operations Research
Proceedings. Springer, 2015.
[ bib ]
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[22]
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D. Horn, T. Wagner, D. Biermann, C. Weihs, and B. Bischl.
Model-based multi-objective optimization: Taxonomy, multi-point
proposal, toolbox and benchmark.
In A. Gaspar-Cunha, C. Henggeler Antunes, and C. C. Coello, editors,
Evolutionary Multi-Criterion Optimization (EMO), volume 9018 of
Lecture Notes in Computer Science, pages 64–78. Springer, 2015.
[ bib |
DOI |
.pdf |
http ]
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[21]
|
B. Bischl, J. Schiffner, and C. Weihs.
Benchmarking classification algorithms on high-performance computing
clusters.
In M. Spiliopoulou, L. Schmidt-Thieme, and R. Janning, editors,
Data Analysis, Machine Learning and Knowledge Discovery, Studies in
Classification, Data Analysis, and Knowledge Organization, pages 23–31.
Springer, 2014.
[ bib |
DOI |
.pdf |
http ]
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[20]
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B. Bischl, S. Wessing, N. Bauer, K. Friedrichs, and C. Weihs.
MOI-MBO: Multiobjective infill for parallel model-based
optimization.
In P. M. Pardalos, M. G. Resende, C. Vogiatzis, and J. L. Walteros,
editors, Learning and Intelligent Optimization, Lecture Notes in
Computer Science, pages 173–186. Springer, 2014.
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DOI |
http ]
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[19]
|
P. Kerschke, M. Preuss, C. Hernández, O. Schütze, J.-Q. Sun, C. Grimme,
G. Rudolph, B. Bischl, and H. Trautmann.
Cell mapping techniques for exploratory landscape analysis.
In Proceedings of the EVOLVE 2014: A Bridge between
Probability, Set Oriented Numerics, and Evolutionary Computation, pages
115–131. Springer, 2014.
[ bib |
.pdf ]
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[18]
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O. Meyer, B. Bischl, and C. Weihs.
Support vector machines on large data sets: Simple parallel
approaches.
In M. Spiliopoulou, L. Schmidt-Thieme, and R. Janning, editors,
Data Analysis, Machine Learning and Knowledge Discovery, Studies in
Classification, Data Analysis, and Knowledge Organization, pages 87–95.
Springer, 2014.
[ bib |
DOI |
.pdf |
http ]
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[17]
|
I. Vatolkin, B. Bischl, G. Rudolph, and C. Weihs.
Statistical comparison of classifiers for multi-objective feature
selection in instrument recognition.
In M. Spiliopoulou, L. Schmidt-Thieme, and R. Janning, editors,
Data Analysis, Machine Learning and Knowledge Discovery, Studies in
Classification, Data Analysis, and Knowledge Organization, pages 171–178.
Springer, 2014.
[ bib |
DOI |
.pdf |
http ]
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[16]
|
S. Hess, T. Wagner, and B. Bischl.
PROGRESS: Progressive reinforcement-learning-based surrogate
selection.
In G. Nicosia and P. Pardalos, editors, Learning and Intelligent
Optimization, Lecture Notes in Computer Science, pages 110–124. Springer,
2013.
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DOI |
.pdf |
http ]
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[15]
|
S. Nallaperuma, M. Wagner, F. Neumann, B. Bischl, O. Mersmann, and
H. Trautmann.
A feature-based comparison of local search and the christofides
algorithm for the travelling salesperson problem.
In Foundations of Genetic Algorithms (FOGA), 2013.
[ bib |
DOI |
.pdf |
http ]
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[14]
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J. van Rijn, B. Bischl, L. Torgo, G. Gao, V. Umaashankar, S. Fischer,
P. Winter, B. Wiswedel, M. Berthold, and J. Vanschoren.
OpenML: A collaborative science platform.
In ECML/PKDD (3), pages 645–649, 2013.
[ bib |
DOI |
.pdf ]
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[13]
|
J. van Rijn, V. Umaashankar, S. Fischer, B. Bischl, L. Torgo, B. Gao,
P. Winter, B. Wiswedel, M. R. Berthold, and J. Vanschoren.
A RapidMiner extension for Open Machine Learning.
In RapidMiner Community Meeting and Conference (RCOMM),
pages 59–70, 2013.
[ bib |
.pdf ]
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[12]
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B. Bischl, O. Mersmann, H. Trautmann, and M. Preuss.
Algorithm selection based on exploratory landscape analysis and
cost-sensitive learning.
In Genetic and Evolutionary Computation Conference (GECCO),
2012.
[ bib |
DOI |
.pdf |
http ]
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[11]
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O. Mersmann, B. Bischl, J. Bossek, H. Trautmann, W. M., and F. Neumann.
Local search and the traveling salesman problem: A feature-based
characterization of problem hardness.
In Learning and Intelligent Optimization Conference (LION),
2012.
[ bib |
DOI |
.pdf ]
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[10]
|
J. Schiffner, B. Bischl, and C. Weihs.
Bias-variance analysis of local classification methods.
In W. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, and J. Kunze,
editors, Challenges at the Interface of Data Analysis, Computer Science,
and Optimization, Studies in Classification, Data Analysis, and Knowledge
Organization, pages 49–57, Berlin Heidelberg, 2012. Springer.
[ bib |
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.pdf |
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[9]
|
C. Weihs, O. Mersmann, B. Bischl, A. Fritsch, H. Trautmann, T. Karbach, and
B. Spaan.
A case study on the use of statistical classification methods in
particle physics.
In W. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, and J. Kunze,
editors, Challenges at the Interface of Data Analysis, Computer Science,
and Optimization, Studies in Classification, Data Analysis, and Knowledge
Organization, pages 69–77, Berlin Heidelberg, 2012. Springer.
[ bib |
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http ]
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[8]
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O. Mersmann, B. Bischl, H. Trautmann, M. Preuss, C. Weihs, and G. Rudolph.
Exploratory landscape analysis.
In N. Krasnogor, editor, Proceedings of the 13th annual
conference on genetic and evolutionary computation (GECCO
11), pages 829–836, New York, NY, USA, 2011. ACM.
[ bib |
DOI |
.pdf |
http ]
|
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[7]
|
C. Weihs, K. Friedrichs, and B. Bischl.
Statistics for hearing aids: Auralization.
In Second Bilateral German-Polish Symposium on Data Analysis and
its Applications (GPSDAA), 2011.
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[6]
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B. Bischl, O. Mersmann, and H. Trautmann.
Resampling methods in model validation.
In T. Bartz-Beielstein, M. Chiarandini, L. Paquete, and M. Preuss,
editors, WEMACS – Proceedings of the Workshop on Experimental Methods
for the Assessment of Computational Systems, Technical Report TR 10-2-007.
Department of Computer Science, TU Dortmund University, 2010.
[ bib |
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[5]
|
B. Bischl, M. Eichhoff, and C. Weihs.
Selecting groups of audio features by statistical tests and the group
lasso.
In 9. ITG Fachtagung Sprachkommunikation, Berlin, Offenbach,
2010. VDE Verlag.
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[4]
|
B. Bischl, I. Vatolkin, and M. Preuss.
Selecting small audio feature sets in music classification by means
of asymmetric mutation.
In PPSN XI: Proceedings of the 11th International Conference on
Parallel Problem Solving from Nature, volume 6238 of Lecture Notes in
Computer Science, pages 314–323. Springer, 2010.
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[3]
|
G. Szepannek, M. Gruhne, B. Bischl, S. Krey, T. Harczos, F. Klefenz,
C. Dittmar, and C. Weihs.
Perceptually based phoneme recognition in popular music.
In H. Locarek-Junge and C. Weihs, editors, Classification as a
Tool for Research, volume 40 of Studies in Classification, Data
Analysis, and Knowledge Organization, pages 751–758, Berlin Heidelberg,
2010. Springer.
[ bib |
DOI |
.pdf |
http ]
|
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[2]
|
C. Weihs, M. O., B. Bischl, A. Fritsch, H. Trautmann, T.-M. Karbach, and
B. Spaan.
A case study on the use of statistical classification methods in
particle physics.
In MSDM2010, Tunis, 2010.
[ bib |
.pdf ]
|
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[1]
|
G. Szepannek, B. Bischl, and C. Weihs.
On the combination of locally optimal pairwise classifiers.
In Machine Learning and Data Mining in Pattern Recognition,
volume 4571 of Lecture Notes in Computer Science, pages 104–116,
Berlin Heidelberg, 2007. Springer.
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.pdf |
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