Authors: Morik, Katharina
Stolpe, Marco
Editors: Gunopulos, D.
Title: Learning from Label Proportions by Optimizing Cluster Model Selection
Language (ISO): en
Abstract: In a supervised learning scenario, we learn a mapping from input to output values, based on labeled examples. Can we learn such a mapping also from groups of unlabeled observations, only knowing, for each group, the proportion of observations with a particular label? Solutions have real world applications. Here, we consider groups of steel sticks as samples in quality control. Since the steel sticks cannot be marked individually, for each group of sticks it is only known how many sticks of high (low) quality it contains. We want to predict the achieved quality for each stick before it reaches the final production station and quality control, in order to save resources. We define the problem of learning from label proportions and present a solution based on clustering. Our method empirically shows a better prediction performance than recent approaches based on probabilistic SVMs, Kernel k-Means or conditional exponential models.
URI: http://hdl.handle.net/2003/29343
http://dx.doi.org/10.17877/DE290R-3964
Issue Date: 2012-02-28
Is part of: ECML PKDD 2011, Part III
Appears in Collections:Sonderforschungsbereich (SFB) 876

Files in This Item:
File Description SizeFormat 
stolpe_morik_2011a.pdfDNB795.58 kBAdobe PDFView/Open


This item is protected by original copyright



All resources in the repository are protected by copyright.