CONF Mayo-More99b/IDIAP Combinatorial Approach for Data Binarization Mayoraz, Eddy Moreira, Miguel Zytkow, J. Ed. Rauch, J. Ed. EXTERNAL http://publications.idiap.ch/attachments/reports/1999/rr99-08.pdf PUBLIC http://publications.idiap.ch/index.php/publications/showcite/mayo-more99 Related documents Principles of Data Mining and Knowledge Discovery: third european conference; proceedings / PKDD'99 Lecture Notes in Artificial Intelligence 1704 442-447 1999 Springer IDIAP-RR 99-08 This 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. REPORT Mayo-More99/IDIAP Combinatorial Approach for Data Binarization Mayoraz, Eddy Moreira, Miguel EXTERNAL http://publications.idiap.ch/attachments/reports/1999/rr99-08.pdf PUBLIC Idiap-RR-08-1999 1999 IDIAP This 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.