TY - GEN
T1 - White Box vs. Black Box Modeling
T2 - 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019
AU - Affenzeller, Michael
AU - Burlacu, Bogdan
AU - Dorfer, Viktoria
AU - Dorl, Sebastian
AU - Halmerbauer, Gerhard
AU - Königswieser, Tilman
AU - Kommenda, Michael
AU - Vetter, Julia
AU - Winkler, Stephan
PY - 2020
Y1 - 2020
N2 - Black box machine learning techniques are methods that produce models which are functions of the inputs and produce outputs, where the internal functioning of the model is either hidden or too complicated to be analyzed. White box modeling, on the contrary, produces models whose structure is not hidden, but can be analyzed in detail. In this paper we analyze the performance of several modern black box as well as white box machine learning methods. We use them for solving several regression and classification problems, namely a set of benchmark problems of the PBML test suite, a medical data set, and a proteomics data set. Test results show that there is no method that is clearly better than the others on the benchmark data sets, on the medical data set symbolic regression is able to find the best classifiers, and on the proteomics data set the black box modeling methods clearly find better prediction models.
AB - Black box machine learning techniques are methods that produce models which are functions of the inputs and produce outputs, where the internal functioning of the model is either hidden or too complicated to be analyzed. White box modeling, on the contrary, produces models whose structure is not hidden, but can be analyzed in detail. In this paper we analyze the performance of several modern black box as well as white box machine learning methods. We use them for solving several regression and classification problems, namely a set of benchmark problems of the PBML test suite, a medical data set, and a proteomics data set. Test results show that there is no method that is clearly better than the others on the benchmark data sets, on the medical data set symbolic regression is able to find the best classifiers, and on the proteomics data set the black box modeling methods clearly find better prediction models.
UR - http://www.scopus.com/inward/record.url?scp=85084004830&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-45093-9_35
DO - 10.1007/978-3-030-45093-9_35
M3 - Conference contribution
AN - SCOPUS:85084004830
SN - 9783030450922
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 288
EP - 295
BT - Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
A2 - Moreno-Díaz, Roberto
A2 - Quesada-Arencibia, Alexis
A2 - Pichler, Franz
PB - Springer
Y2 - 17 February 2019 through 22 February 2019
ER -