Various approaches for dealing with missing data have been developed so far. In this paper, two strategies are proposed for cost-sensitive iterative imputing missing values with o...
Handling missing data is a critical step to ensuring good results in data mining. Like most data mining algorithms, existing privacy-preserving data mining algorithms assume data ...
—DNA microarray technologies provide means for monitoring in the order of tens of thousands of gene expression levels quantitatively and simultaneously. However data generated in...
We propose a novel second order cone programming formulation for designing robust classifiers which can handle uncertainty in observations. Similar formulations are also derived f...
Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyy...
Abstract. This paper presents a new methodology for missing value imputation in a database. The methodology combines the outputs of several Self-Organizing Maps in order to obtain ...
Antti Sorjamaa, Francesco Corona, Yoan Miche, Paul...