Details

Autor(en) / Beteiligte

• Nießl, Christina
• Herrmann, Moritz
• Wiedemann, Chiara
• Casalicchio, Giuseppe
• Boulesteix, Anne‐Laure

Titel

Over‐optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results

Ist Teil von

• Wiley interdisciplinary reviews. Data mining and knowledge discovery, 2022-03, Vol.12 (2), p.e1441-n/a

Ort / Verlag

Hoboken, USA: Wiley Periodicals, Inc

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Wiley Online Library

Beschreibungen/Notizen

• In recent years, the need for neutral benchmark studies that focus on the comparison of methods coming from computational sciences has been increasingly recognized by the scientific community. While general advice on the design and analysis of neutral benchmark studies can be found in recent literature, a certain flexibility always exists. This includes the choice of data sets and performance measures, the handling of missing performance values, and the way the performance values are aggregated over the data sets. As a consequence of this flexibility, researchers may be concerned about how their choices affect the results or, in the worst case, may be tempted to engage in questionable research practices (e.g., the selective reporting of results or the post hoc modification of design or analysis components) to fit their expectations. To raise awareness for this issue, we use an example benchmark study to illustrate how variable benchmark results can be when all possible combinations of a range of design and analysis options are considered. We then demonstrate how the impact of each choice on the results can be assessed using multidimensional unfolding. In conclusion, based on previous literature and on our illustrative example, we claim that the multiplicity of design and analysis options combined with questionable research practices lead to biased interpretations of benchmark results and to over‐optimistic conclusions. This issue should be considered by computational researchers when designing and analyzing their benchmark studies and by the scientific community in general in an effort towards more reliable benchmark results. This article is categorized under: Technologies > Visualization Technologies > Data Preprocessing Technologies > Structure Discovery and Clustering When conducting a benchmark study, researchers are faced with many choices that relate to (1) the general aim of the study, (2) the design of the study, or (3) the analysis of the performance results. As a consequence, benchmark results are highly variable. In combination with questionable research practices, this can lead to biased interpretations and over‐optimistic conclusions.