000082507 001__ 82507
000082507 005__ 20180317093227.0
000082507 037__ $$aREP_WORK
000082507 245__ $$aCombinatorial Approach for Data Binarization
000082507 269__ $$a1999
000082507 260__ $$bIDIAP$$c1999
000082507 336__ $$aReports
000082507 520__ $$aThis paper addresses the problem of transforming arbitrary data into binary data. This is intended as preprocessing for a supervised classification task. As a binary mapping compresses the total information of the dataset, the goal here is to design such a mapping that maintains most of the information relevant to the classification problem. Most of the existing approaches to this problem are based on correlation or entropy measures between one individual binary variable and the partition into classes. On the contrary, the approach proposed here is based on a global study of the combinatorial property of a set of binary variable.
000082507 6531_ $$alearning
000082507 6531_ $$aeddy
000082507 6531_ $$amiguel
000082507 700__ $$aMayoraz, Eddy
000082507 700__ $$aMoreira, Miguel
000082507 8564_ $$uhttp://publications.idiap.ch/downloads/reports/1999/rr99-08.pdf$$zURL
000082507 8564_ $$s228199$$uhttps://infoscience.epfl.ch/record/82507/files/rr99-08.pdf$$zn/a
000082507 909CO $$ooai:infoscience.tind.io:82507$$preport$$pSTI
000082507 909C0 $$0252189$$pLIDIAP$$xU10381
000082507 937__ $$aEPFL-REPORT-82507
000082507 970__ $$aMayo-More99/LIDIAP
000082507 973__ $$aEPFL$$sPUBLISHED
000082507 980__ $$aREPORT