Set Approximation in Incomplete Data

Approximation reductions in an incomplete variable precision multigranulation rough set

This paper deals with approaches to the granular space reductions in the variable precision multigranulation rough set model. The main objective of this study is to extend four kinds of the granular space reductions called a tolerance relations optimistic multigranulation  ower approximation distribution reduction, a tolerance relations optimistic multigranulation  upper approximation distrib...

Image Reconstruction from Incomplete Data

Mining Incomplete Data - A Rough Set Approach

Rough set approach to incomplete numerical data

The theory rough set successfully implemented diberbagai sector , but rough set model classical can only associated with the data complete and set of data in symbolic form ( Jianhua , dai , 2013 ) . Research by adopting the theory rough set conducted in attribute numerical and the value of an attribute lost ( Jerzy w , Grzymala-Buse and Zdzislaw S , Hippe , Nov .2011 ) .In this research discuss...

Feature Set Embedding for Incomplete Data

We present a new learning strategy for classification problems in which train and/or test data suffer from missing features. In previous work, instances are represented as vectors from some feature space and one is forced to impute missing values or to consider an instance-specific subspace. In contrast, our method considers instances as sets of (feature,value) pairs which naturally handle the ...