  With the popularity of Machine Learning (ML) solutions, algorithms and data
have been released faster than the capacity of processing them. In this
context, the problem of Algorithm Recommendation (AR) is receiving a
significant deal of attention recently. This problem has been addressed in the
literature as a learning task, often as a Meta-Learning problem where the aim
is to recommend the best alternative for a specific dataset. For such, datasets
encoded by meta-features are explored by ML algorithms that try to learn the
mapping between meta-representations and the best technique to be used. One of
the challenges for the successful use of ML is to define which features are the
most valuable for a specific dataset since several meta-features can be used,
which increases the meta-feature dimension. This paper presents an empirical
analysis of Feature Selection and Feature Extraction in the meta-level for the
AR problem. The present study was focused on three criteria: predictive
performance, dimensionality reduction, and pipeline runtime. As we verified,
applying Dimensionality Reduction (DR) methods did not improve predictive
performances in general. However, DR solutions reduced about 80% of the
meta-features, obtaining pretty much the same performance as the original setup
but with lower runtimes. The only exception was PCA, which presented about the
same runtime as the original meta-features. Experimental results also showed
that various datasets have many non-informative meta-features and that it is
possible to obtain high predictive performance using around 20% of the original
meta-features. Therefore, due to their natural trend for high dimensionality,
DR methods should be used for Meta-Feature Selection and Meta-Feature
Extraction.
