Automatic learning algorithm selection for classification via
  convolutional neural networks
Sebastian Maldonado Carla Vairetti Ignacio Figueroa
As in any other task, the process of building machine learning models can
benefit from prior experience. Meta-learning for classifier selection gains
knowledge from characteristics of different datasets and/or previous
performance of machine learning techniques to make better decisions for the
current modeling process. Meta-learning approaches first collect meta-data that
describe this prior experience and then use it as input for an algorithm
selection model. In this paper, however, we propose an automatic learning
scheme in which we train convolutional networks directly with the information
of tabular datasets for binary classification. The goal of this study is to
learn the inherent structure of the data without identifying meta-features.
Experiments with simulated datasets show that the proposed approach achieves
nearly perfect performance in identifying linear and nonlinear patterns,
outperforming the traditional two-step method based on meta-features. The
proposed method is then applied to real-world datasets, making suggestions
about the best classifiers that can be considered based on the structure of the
data.