The diversity of an organism's B- and T-cell repertoires is both clinically important and a key measure of immunological complexity. However, diversity is hard to estimate by current methods, because of inherent uncertainty in the number of B- and T-cell clones that will be missing from a blood or tissue sample by chance (the missing-species problem), inevitable sampling bias, and experimental ...

Missing values in inputs, outputs cannot be handled by the original data envelopment analysis (DEA) models. In this paper we introduce an approach based on interval DEA that allows the evaluation of the units with missing values along with the other units with available crisp data. The missing values are replaced by intervals in which the unknown values are likely to belong. The constant bounds...

There are many situations in data classification where the data vector to be classified is partially corrupted, or otherwise incomplete. In this case the optimal estimate for each class probability output, for any given set of missing data components, can be obtained by calculating its expected value. However, this means that classifiers whose expected outputs do not have a closed form expressi...

Friedman’s test is used traditionally in statistics for testing independence between k orderings (k > 2). In the paper we suggest how to generalize Friedman’s test for situations with missing information or non-comparable outputs.

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 an accurate filling for the missing values. The maps are combined using MultiResponse Sparse Regression and the Hannan-Quinn Information Criterion. The new combination methodology removes the need for any lengthy cross-validati...

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 an accurate filling for the missing values. The maps are combined using MultiResponse Sparse Regression and the Hannan-Quinn Information Criterion. The new combination methodology removes the need for any lengthy cross-validati...

The evaluation of productivity of educational units during the last decades has become an important priority for many countries. A current approach considers the schools as production units that use multiple inputs and produce multiple outputs. Data Envelopment Analysis (DEA) is a very effective methodology for the estimation of relative efficiencies in the presence of multiple inputs and outpu...

This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random. A non-parametric perspective is adopted by defining a modified risk taking into account the uncertainty of the predicted outputs when missing values are involved. It is shown that this approach generalizes the approach of mean imputation ...

In this paper, we propose the problem of domain transfer structured output learning and the first solution to solve it. The problem is defined on two different data domains sharing the same input and output spaces, named as source domain and target domain. The outputs are structured, and for the data samples of the source domain, the corresponding outputs are available, while for most data samp...

Kendall’s coefficient of concordance is used traditionally in statistics for measuring agreement between k orderings (k > 2).A new measure of concordance which generalizes Kendall’s coefficient is proposed. The suggested coefficient could be used in situations with missing information or noncomparable outputs. © 2006 Elsevier B.V. All rights reserved.