TY  - RPRT
ID  - ma:straninger
T1  - Training of mixed deep classifiers of symbolical rules and neural networks - Training von gemischten Regel- und neuronalen Netzwerk-Klassifizierern
A1  - Straninger, Andreas
Y1  - 2016
M1  - Master thesis
T2  - TU Darmstadt, Knowledge Engineering Group
UR  - /lehre/arbeiten/master/2016/Straninger_Andreas.pdf
N2  - This thesis deals with combining a decision list classifiers with neural networks in order to gain a mixed classification
model, which can be further transformed into a pure multi-layered decision list classifier. The resulting classifier has the
potential of increase the accuracy especially for deep network topologies. In contrast to neural networks, the rules in
decision lists are better understandable by humans.
This thesis shows the relations of the mixed network to ensemble trainers (especially gradient boosting), fuzzy neural
networks and other descriptive rule learning algorithms.
The contribution of this thesis is a method for performing an online training of neural networks and decision lists. For
this purpose, a way of training decision lists with small portions of data (mini-batches) instead of complete data sets is
introduced. Further contributions include the way of combining rule learning algorithms with neural networks similar to
gradient boosting and a binary backpropagation scheme for symbolical methods.
During evaluation with the MNIST dataset, it is shown that existing classifiers like C4.5 and RIPPER benefit from the
results of the decision list layers and can increase their performance. In comparison to existing approaches, a model with
a reduced complexity can be obtained.
M1  - betreuer={ELM}
ER  -