Authors:
Fabian Bürger
and
Josef Pauli
Affiliation:
University of Duisburg-Essen, Germany
Keyword(s):
Model Selection, Manifold Learning, Evolutionary Optimization, Classification.
Related
Ontology
Subjects/Areas/Topics:
Combinatorial Optimization
;
Embedding and Manifold Learning
;
Evolutionary Computation
;
Feature Selection and Extraction
;
Multiclassifier Fusion
;
Pattern Recognition
;
Theory and Methods
Abstract:
Many complex and high dimensional real-world classification problems require a carefully chosen set of features,
algorithms and hyperparameters to achieve the desired generalization performance. The choice of a
suitable feature representation has a great effect on the prediction performance. Manifold learning techniques
– like PCA, Isomap, Local Linear Embedding (LLE) or Autoencoders – are able to learn a better suitable
representation automatically. However, the performance of a manifold learner heavily depends on the dataset.
This paper presents a novel automatic optimization framework that incorporates multiple manifold learning
algorithms in a holistic classification pipeline together with feature selection and multiple classifiers with arbitrary
hyperparameters. The highly combinatorial optimization problem is solved efficiently using evolutionary
algorithms. Additionally, a multi-pipeline classifier based on the optimization trajectory is presented. The
evaluation on several da
tasets shows that the proposed framework outperforms the Auto-WEKA framework
in terms of generalization and optimization speed in many cases.
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