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Author:Mangs, Karl Johan
Title:Classifying Individual-Level Migration Behavior Using Multivariate Methods
Användning av multivarianta metoder för klassificering av individuell migration
Publication type:Master's thesis
Publication year:2010
Pages:vii + 97 + [21]      Language:   eng
Department/School:Matematiikan ja systeemianalyysin laitos
Main subject:Sovellettu matematiikka   (Mat-2)
Supervisor:Ehtamo, Harri
Instructor:Röytiö, Petri
OEVS:
Electronic archive copy is available via Aalto Thesis Database.
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Location:P1 Ark Aalto  212   | Archive
Keywords:migration
classification
data mining
migration
klassificering
informationsutvinning
Abstract (eng): The decision to migrate tends to be coupled with significant changes in the migrant's personal life.
Consequently, the ability to forecast which individuals are about to migrate would offer companies an excellent opportunity to benefit from this fact in their prospecting and marketing.
Targeting imminent migrants with well-timed and suitable offers would yield higher readership levels due to the more interested prospects.

The aim of this thesis was to develop a method for classifying the adult Finnish population into one of the following categories: those who will migrate within the following six months, and those who will migrate at a later date.
Utilizing the k-nearest neighbour algorithm, generalized linear models, support vector machines, optimally-pruned extreme learning machines, and their ensembles this thesis sought to analyze which approach offers the best classification result and whether there are significant differences regarding the practical implementations of these methods.

Provided access to the full extent of the Population Information System database, this thesis was offered a premium opportunity to model the Finnish migration phenomenon through a large sample size and an extensive set of features.
On the other hand, this created a demand for feature selection, which in this thesis was conducted based on a literature review.
Moreover, the Delta Test was implemented as a method for pruning the selected shortlist from additional redundant features.

The results revealed that there exists a subset of features that have a substantial impact on the classification accuracy.
In addition, it was concluded that all of the implemented methods were capable of producing satisfactory results and that their performances were rather similar.
However, considering that this thesis was able to highlight some very distinct practical differences regarding the methods, these findings provide valuable insights concerning future implementations.
Abstract (swe): En individs beslut att migrera är ofta kopplat till andra stora förändringar i livet.
Följaktligen är förmågan att förutse vem som kommer att migrera ger därför värdefull information som kan utnyttjas av företag i sin prospekteringsverksamhet och marknadsföring.
Genom att rikta sig till denna specifika målgrupp i rätt stund med ändamålsenlig budskap når företagen en betydligt större läsekrets eftersom mottagarna är mer receptiva.

Målet med denna avhandling är att utveckla en metod för att klassificera Finlands vuxna befolkning i två kategorier: de som kommer att migrera inom det närmaste halvåret och de som kommer att migrera senare.
I avhandlingen användes k-närmaste granne algoritmen, generaliserade linjära modeller, stödvektormaskiner, OP-ELM (Optimally-Pruned Extreme Learning Machine) modeller samt kombinationer av dessa för att analysera vilken metod som ger det bästa resultatet, samt för att analysera huruvida det finns betydelsefulla skillnader i den praktiska tillämpningen av dessa metoder.

Gränslös tillgång till Befolkningsregistercentralens databas gav ett unikt tillfälle att studera den finländska migrationen eftersom både undersökningens underlag samt antalet användbara variabler var stort.
Detta innebar dock att antalet variabler måste begränsas, vilket främst gjordes påbasen av litteraturstudien.
Utöver detta värderades huruvida Delta-testet kunde användas för att utesluta irrelevanta variabler.

Resultaten visade att det finns en mängd variabler som inverkar på hur korrekt klassificeringen av migranterna kommer att vara.
Det visade sig också att klassificeringsmetoderna genererade tillfredställande och sinsemellan liknande resultat.
Avhandlingen visar också tydligt att det finns stora praktiska skillnader bland metoderna.
Dessa skillnader samt resultaten ger värdefull vägledning angående framtida praktiska tillämpningar.
ED:2010-11-17
INSSI record number: 41319
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