Authors:
Fabian Bürger
and
Josef Pauli
Affiliation:
University of Duisburg-Essen, Germany
Keyword(s):
Model Selection, Representation Learning, Classification, Evolutionary Optimization.
Related
Ontology
Subjects/Areas/Topics:
Combinatorial Optimization
;
Embedding and Manifold Learning
;
Evolutionary Computation
;
Feature Selection and Extraction
;
Model Selection
;
Pattern Recognition
;
Theory and Methods
Abstract:
The development of classification systems that meet the desired accuracy levels for real world-tasks applications
requires a lot of expertise. Numerous challenges, like noisy feature data, suboptimal algorithms and
hyperparameters, degrade the generalization performance. On the other hand, almost countless solutions have
been developed, e.g. feature selection, feature preprocessing, automatic algorithm and hyperparameter selection.
Furthermore, representation learning is emerging to automatically learn better features. The challenge
of finding a suitable and tuned algorithm combination for each learning task can be solved by automatic optimization
frameworks. However, the more components are optimized simultaneously, the more complex their
interplay becomes with respect to the generalization performance and optimization run time. This paper analyzes
the interplay of the components in a holistic framework which optimizes the feature subset, feature
preprocessing, representation learni
ng, classifiers and all hyperparameters. The evaluation on a real-world
dataset that suffers from the curse of dimensionality shows the potential benefits and risks of such holistic
optimization frameworks.
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