Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy - Sorbonne Université Access content directly
Conference Papers Year : 2020

Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy

Abstract

Exploratory landscape analysis (ELA) supports supervised learning approaches for automated algorithm selection and configuration by providing sets of features that quantify the most relevant characteristics of the optimization problem at hand. In black-box optimization, where an explicit problem representation is not available, the feature values need to be approximated from a small number of sample points. In practice, uniformly sampled random point sets and Latin hypercube constructions are commonly used sampling strategies. In this work, we analyze how the sampling method and the sample size influence the quality of the feature value approximations and how this quality impacts the accuracy of a standard classification task. While, not unexpectedly, increasing the number of sample points gives more robust estimates for the feature values, to our surprise we find that the feature value approximations for different sampling strategies do not converge to the same value. This implies that approximated feature values cannot be interpreted independently of the underlying sampling strategy. As our classification experiments show, this also implies that the feature approximations used for training a classifier must stem from the same sampling strategy as those used for the actual classification tasks. As a side result we show that classifiers trained with feature values approximated by Sobol' sequences achieve higher accuracy than any of the standard sampling techniques. This may indicate improvement potential for ELA-trained machine learning models.
Fichier principal
Vignette du fichier
PPSN_Doerr_paper_30.pdf (591.99 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02935385 , version 1 (10-09-2020)

Identifiers

Cite

Quentin Renau, Carola Doerr, Johann Dreo, Benjamin Doerr. Exploratory Landscape Analysis is Strongly Sensitive to the Sampling Strategy. Parallel Problem Solving from Nature – PPSN XVI (PPSN 2020), Sep 2020, Leiden, Netherlands. pp.139-153, ⟨10.1007/978-3-030-58115-2_10⟩. ⟨hal-02935385⟩
44 View
100 Download

Altmetric

Share

Gmail Facebook X LinkedIn More