OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms

Viktor Schmuck, David Meredith

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Abstrakt

We propose a genetic algorithm (GA), OPTISIA, for efficiently finding optimal parameter combinations when running OMNISIA, a program that implements a family of analysis and compression algorithms based on the SIA point-set pattern discovery algorithm. The GA, when given a point-set representation of a piece of music as input, runs OMNISIA multiple times, attempting to evolve a combination of parameter values that achieves the highest compression factor on the input piece. When evaluated on two musicological tasks, the system consistently selected well-performing parameters for Forth’s algorithm compared to combinations found in published evaluations on the same musicological tasks.
OriginalsprogEngelsk
TitelMachine Learning and Knowledge Discovery in Databases : International Workshops of ECML PKDD 2019 Würzburg, Germany, September 16–20, 2019 Proceedings, Part II
RedaktørerPeggy Cellier, Kurt Driessens
Antal sider8
Vol/bind1168
UdgivelsesstedCham, Switzerland
ForlagSpringer
Publikationsdato2020
Sider509-516
ISBN (Trykt)978-3-030-43886-9
ISBN (Elektronisk)978-3-030-43887-6
DOI
StatusUdgivet - 2020
BegivenhedInternational Workshop on Machine Learning and Music - Würzburg, Tyskland
Varighed: 16 sep. 201916 sep. 2019
Konferencens nummer: 12
https://musml2019.weebly.com/

Konference

KonferenceInternational Workshop on Machine Learning and Music
Nummer12
LandTyskland
ByWürzburg
Periode16/09/201916/09/2019
Internetadresse
NavnCommunications in Computer and Information Science
Vol/bind1168
ISSN1865-0929

Fingeraftryk Dyk ned i forskningsemnerne om 'OPTISIA: An Evolutionary Approach to Parameter Optimisation in a Family of Point-Set Pattern-Discovery Algorithms'. Sammen danner de et unikt fingeraftryk.

Citationsformater