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Classifying Individual-Level Migration Behavior Using Multivariate Methods

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.advisor Röytiö, Petri
dc.contributor.author Mangs, Karl Johan
dc.date.accessioned 2020-12-23T13:08:36Z
dc.date.available 2020-12-23T13:08:36Z
dc.date.issued 2010
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/98943
dc.description.abstract 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. en
dc.format.extent vii + 97 + [21]
dc.language.iso en en
dc.title Classifying Individual-Level Migration Behavior Using Multivariate Methods en
dc.title Användning av multivarianta metoder för klassificering av individuell migration sv
dc.contributor.school Perustieteiden korkeakoulu fi
dc.contributor.school School of Science en
dc.contributor.department Matematiikan ja systeemianalyysin laitos fi
dc.subject.keyword migration en
dc.subject.keyword migration sv
dc.subject.keyword classification en
dc.subject.keyword klassificering sv
dc.subject.keyword data mining en
dc.subject.keyword informationsutvinning sv
dc.identifier.urn URN:NBN:fi:aalto-2020122357770
dc.programme.major Sovellettu matematiikka fi
dc.programme.mcode Mat-2 fi
dc.type.ontasot Master's thesis en
dc.type.ontasot Pro gradu -tutkielma fi
dc.contributor.supervisor Ehtamo, Harri
local.aalto.openaccess no
local.aalto.digifolder Aalto_04584
dc.rights.accesslevel closedAccess
local.aalto.idinssi 41319
dc.type.publication masterThesis
dc.type.okm G2 Pro gradu, diplomityö
local.aalto.digiauth ask

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