000030488 001__ 30488
000030488 005__ 20150420164420.0
000030488 0248_ $$aoai:doc.rero.ch:20121025093058-TD$$particle$$ppostprint$$prero_explore$$phesge$$zreport$$zthesis$$zcdu004$$zbook$$zjournal$$zcdu16$$zhegge$$zpreprint$$zcdu1$$zdissertation$$zthesis_urn$$zcdu34
000030488 041__ $$aeng
000030488 080__ $$a004
000030488 100__ $$aWoznica, Adam$$uCUI, Université de Genève
000030488 245__ $$9eng$$aModel mining for robust feature selection
000030488 269__ $$c2012
000030488 520__ $$9eng$$aA common problem with most of the feature selection methods is that they often produce feature sets--models--that are not stable with respect to slight variations in the training data. Different authors tried to improve the feature selection stability using ensemble methods which aggregate different feature sets into a single model. However, the existing ensemble feature selection methods suffer from two main shortcomings: (i) the aggregation treats the features independently and does not account for their interactions, and (ii) a single feature set is returned, nevertheless, in various applications there might be more than one feature sets, potentially redundant, with similar information content. In this work we address these two limitations. We present a general framework in which we mine over different feature models produced from a given dataset in order to extract patterns over the models. We use these patterns to derive more complex feature model aggregation strategies that account for feature interactions, and identify core and distinct feature models. We conduct an extensive experimental evaluation of the proposed framework where we demonstrate its effectiveness over a number of high-dimensional problems from the fields of biology and text-mining.
000030488 695__ $$9eng$$afeature selection ; stability ; model mining ; high-dimensional
data ; classification
000030488 700__ $$aNguyen, Phong$$uCUI, Université de Genève
000030488 700__ $$aKalousis, Alexandros$$uHaute école de gestion de Genève
000030488 773__ $$g2012///913-921$$tKDD '12 Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
000030488 775__ $$gPublished version$$ohttp://dx.doi.org/10.1145/2339530.2339674
000030488 8564_ $$fWoznicaNguyenKalousis-KDD2012-ModelMiningForRobustFS.pdf$$qapplication/pdf$$s266087$$uhttp://doc.rero.ch/record/30488/files/WoznicaNguyenKalousis-KDD2012-ModelMiningForRobustFS.pdf$$yorder:1$$zTexte intégral
000030488 918__ $$aHaute école de gestion de Genève$$bCampus de Battelle, Bâtiment F, 7 route de Drize, 1227 Carouge$$cCentre de recherche appliqué en gestion (CRAG)
000030488 919__ $$aHaute école de gestion de Genève$$bGenève$$ddoc.support@rero.ch
000030488 980__ $$aPOSTPRINT$$bHEGGE$$fART_INPROC
000030488 990__ $$a20121025093058-TD