Text

Bayesian Performance Analysis for Algorithm Ranking Comparison

In the field of optimization and machine learning, the statistical assessment of results has played a key role in conducting algorithmic performance comparisons. Classically, null hypothesis statistical tests have been used. However, recently, alternatives based on Bayesian statistics have shown great potential in complex scenarios, especially when quantifying the uncertainty in the comparison. In this work, we delve deep into the Bayesian statistical assessment of experimental results by proposing a framework for the analysis of several algorithms on several problems/instances. To this end, experimental results are transformed to their corresponding rankings of algorithms, assuming that these rankings have been generated by a probability distribution (defined on permutation spaces). From the set of rankings, we estimate the posterior distribution of the parameters of the studied probability models, and several inferences concerning the analysis of the results are examined. Particularly, we study questions related to the probability of having one algorithm in the first position of the ranking or the probability that two algorithms are in the same relative position in the ranking. Not limited to that, the assumptions, strengths, and weaknesses of the models in each case are studied. To help other researchers to make use of this kind of analysis, we provide a Python package and source code implementation at https://zenodo.org/record/6320599 .

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.26 No.6 December 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1281-1292

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

BENCHMARKING
BAYESIAN INFERENCE
EVOLUTIONARY ALGORITHMS
PROBABILISTIC MODELS ON PERMUTATION SPACES

Info Detail Spesifik

10.1109/TEVC.2022.3208110

Pernyataan Tanggungjawab

Jairo Rojas-Delgado, Josu Ceberio, Borja Calvo, Jose A. Lozano

Versi lain/terkait

Lampiran Berkas

Komentar