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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F13%3APU106418%21RIV14-TA0-26230___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
The paper deals with classification of highly imbalanced data with accuracy constraints for the minority class. We solve this problem by our proposed meta-learning method that uses cost-sensitive logistic regression to generate initial candidate models. These models can be used as an initial solutions for various optimization algorithms. This paper is aimed for using Particle Swarm Optimization (PSO) to handle the constrained imbalanced classification problem. Experiments, comparing with Genetic Algorithm (GA), show that the swarm intelligence approach is suitable for this problem and outperforms GA. The paper deals with classification of highly imbalanced data with accuracy constraints for the minority class. We solve this problem by our proposed meta-learning method that uses cost-sensitive logistic regression to generate initial candidate models. These models can be used as an initial solutions for various optimization algorithms. This paper is aimed for using Particle Swarm Optimization (PSO) to handle the constrained imbalanced classification problem. Experiments, comparing with Genetic Algorithm (GA), show that the swarm intelligence approach is suitable for this problem and outperforms GA.
dcterms:title
PSO-based Constrained Imbalanced Data Classification PSO-based Constrained Imbalanced Data Classification
skos:prefLabel
PSO-based Constrained Imbalanced Data Classification PSO-based Constrained Imbalanced Data Classification
skos:notation
RIV/00216305:26230/13:PU106418!RIV14-TA0-26230___
n18:predkladatel
n19:orjk%3A26230
n3:aktivita
n7:S n7:P n7:Z
n3:aktivity
P(ED1.1.00/02.0070), P(TA01010858), S, Z(MSM0021630528)
n3:dodaniDat
n4:2014
n3:domaciTvurceVysledku
n5:3398706 n5:6364241 n5:9970118 n5:3725340
n3:druhVysledku
n15:D
n3:duvernostUdaju
n23:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
100872
n3:idVysledku
RIV/00216305:26230/13:PU106418
n3:jazykVysledku
n10:eng
n3:klicovaSlova
Data mining, imbalance classification, constraints, PSO, Genetic Algorithm
n3:klicoveSlovo
n12:Data%20mining n12:PSO n12:imbalance%20classification n12:constraints n12:Genetic%20Algorithm
n3:kontrolniKodProRIV
[149D8D4E15D8]
n3:mistoKonaniAkce
Spišská Nová Ves
n3:mistoVydani
Spišská Nová Ves
n3:nazevZdroje
Proceedings of the Twelth International Conference on Informatics INFORMATICS'2013
n3:obor
n11:IN
n3:pocetDomacichTvurcuVysledku
4
n3:pocetTvurcuVysledku
4
n3:projekt
n9:ED1.1.00%2F02.0070 n9:TA01010858
n3:rokUplatneniVysledku
n4:2013
n3:tvurceVysledku
Stríž, Rostislav Hruška, Tomáš Zendulka, Jaroslav Hlosta, Martin
n3:typAkce
n22:WRD
n3:zahajeniAkce
2013-11-05+01:00
n3:zamer
n21:MSM0021630528
s:numberOfPages
6
n13:hasPublisher
Technická univerzita v Košiciach
n16:isbn
978-80-8143-127-2
n20:organizacniJednotka
26230