Computational Intelligence Methods in Metalearning

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@PhdThesis{Smid:thesis,
  author =       "Jakub Smid",
  title =        "Computational Intelligence Methods in Metalearning",
  school =       "Faculty of Mathematics and Physics, Charles University
                 in Prague",
  year =         "2016",
  address =      "Prague",
  keywords =     "genetic algorithms, genetic programming, Metalearning,
                 Machine Learning, Metric, Genetic Algorithms, Attribute
                 Assignment",
  URL =          "http://hdl.handle.net/20.500.11956/82405",
  URL =          "https://dspace.cuni.cz/handle/20.500.11956/82405",
  URL =          "https://dspace.cuni.cz/bitstream/handle/20.500.11956/82405/IPTX_2011_2_11320_0_394065_0_123234.pdf",
  size =         "158 pages",
  abstract =     "This thesis focuses on the algorithm selection
                 problem, in which the goal is to recommend machine
                 learning algorithms to a new dataset. The idea behind
                 solving this issue is that algorithm performs similarly
                 on similar datasets. The usual approach is to base the
                 similarity measure on the fixed vector of meta-features
                 extracted out of each dataset. However, as the number
                 of attributes among datasets varies, we may be loosing
                 important information. Herein, we propose a family of
                 algorithms able to handle even the non-propositional
                 representations of datasets. Our methods use the idea
                 of attribute assignment that builds the distance
                 measure between datasets as a sum of distance given by
                 the optimal assignment and an attribute distance
                 measure. Furthermore, we prove that under certain
                 conditions, we can guarantee the resulting dataset
                 distance to be a metric. We carry out a series of
                 meta-learning experiments on the data extracted from
                 the OpenML repository. We build up attribute distance
                 using Genetic Algorithms, Genetic Programming and
                 several regularization techniques such as
                 multi-objectivization, coevolution, and bootstrapping.
                 The experiment indicates that the resulting dataset
                 distance can be successfully applied on the algorithm
                 selection problem. Although we use the proposed
                 distance measures exclusively...",
  abstract =     "Tato prace je zamerena na problematiku vyberu
                 algoritmu, ktera ma za cil doporucit algoritmus
                 strojoveho uceni k nove uloze. Reseni problemu vychazi
                 z myslenky, ze se algoritmy chovaji podobne na
                 podobnych datech. Tato podobnost je casto zalozena na
                 extrakci pevneho poctu metaatributů z kazde ulohy.
                 Vzhledem k tomu, ze pocet atributů se u různych uloh
                 typicky lisi, ztracime tak důlezite informace. V teto
                 praci popiseme tridu algoritmů, ktera dokaze zpracovat
                 take informace o jednotlivych atributech. Nase metody
                 jsou zalozeny na prirazovani atributů. Vysledna
                 vzdalenost mezi ulohami je dana jako soucet vzdalenosti
                 mezi atributy urcenymi optimalnim prirazenim. Dale
                 dokazeme, ze za urcitych podminek můzeme zarucit, ze
                 vysledna vzdalenost mezi ulohami je metrika. Provedeme
                 sadu experimentů na datech extrahovanych z OpenML
                 repozitare. Vytvorime vzdalenost mezi atributy
                 prostrednictvim genetickych algoritmů, genetickeho
                 programovani a nekolika regularizacnich technik, jako
                 je koevoluce a zavedeni vicekriteriality. Vysledky
                 experimentů naznacuji, ze vysledna vzdalenost mezi
                 ulohami můze byt uspesne pouzita na problematiku
                 vyberu algoritmu. Ackoliv jsme nase metody pouzili
                 vyhradne k metauceni, lze je aplikovat i v jinych
                 oblastech. Navrzene algoritmy jsou aplikovatelne
                 kdekoliv, kde mame definovanou vzdalenost...",
  notes =        "Supervisor: Roman Neruda",
}

Genetic Programming entries for Jakub Smid

Citations