A Novel Initial Population Construction Heuristic for the DINOS Subgroup Discovery Algorithm

Autor:	Lenier Reyes-Morales, Diana Martin, Milton García-Borroto, Lisandra Bravo-Ilisástigui
Rok vydání:	2021
Předmět:	education.field_of_study Computer science Heuristic media_common.quotation_subject Population Decision tree Evolutionary algorithm Initialization Quality (business) education Algorithm media_common
Zdroj:	Progress in Artificial Intelligence and Pattern Recognition ISBN: 9783030896904 IWAIPR
DOI:	10.1007/978-3-030-89691-1_27
Popis:	Evolutionary algorithms for subgroup discovery usually randomly initialize the population, which often causes them to spend part of their time evaluating unpromising solutions. This situation causes the algorithm to take more time to converge to good solutions. In this paper, we present a new initial population construction heuristic for DINOS, a genetic subgroup discovery algorithm that mines non-redundant subgroups with high quality in a short time. The proposed heuristic is based on the generation of a collection of decision trees, allowing to obtain an initial population in which all the rules are valid and with a large coverage of the database. The quality of these rules is also high and they contain a large diversity in the attributes used, allowing to deal with problems having a large number of dimensions. The experiments carried out show that the new method allows mining more high-quality and diverse subgroups in a slightly higher computational time.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f24102f1d4de4f5ae6b21558379f508d https://doi.org/10.1007/978-3-030-89691-1_27 Zobrazit plný text záznamu