Representation Optimization with Feature Selection and Manifold Learning in a Holistic Classification Framework

Fabian Bürger, Josef Pauli

2015

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 datasets shows that the proposed framework outperforms the Auto-WEKA framework in terms of generalization and optimization speed in many cases.

Paper Citation

in Harvard Style

Bürger F. and Pauli J. (2015). Representation Optimization with Feature Selection and Manifold Learning in a Holistic Classification Framework . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 35-44. DOI: 10.5220/0005183600350044

in Bibtex Style

@conference{icpram15,
author={Fabian Bürger and Josef Pauli},
title={Representation Optimization with Feature Selection and Manifold Learning in a Holistic Classification Framework},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2015},
pages={35-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005183600350044},
isbn={978-989-758-076-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Representation Optimization with Feature Selection and Manifold Learning in a Holistic Classification Framework
SN - 978-989-758-076-5
AU - Bürger F.
AU - Pauli J.
PY - 2015
SP - 35
EP - 44
DO - 10.5220/0005183600350044