Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values. Possible solutions are given by imputation methods, complete case analysis and a newly suggested importance measure. However, it is unknown t...

The ability to make decisions and to assess po­ tential courses of action is a comer-stone of many AI applications, and usually this requires explicit information about the decision-maker's prefer­ ences. In many applications, preference elic­ itation is a serious bottleneck. The user ei­ ther does not have the time, the knowledge, or the expert support required to specify complex multi-attribu...

In this work, we present the benchmark generator PUBO $$_i$$ , Polynomial Unconstrained Binary Optimization, that combines subproblems to create instances of pseudo-boolean optimization problems. Any mono-objective pseudoboolean functions including existing classical problems can be expressed with Walsh functions. The tune main features such as problem dimension, non-linearity degree, and neutr...

results age was selected as the important factor based on all 7 statistics. exclusion of age variable decreased c index from 0.75 to 0.68 and r square from 0.25 to 0.15. duration of hospitalization was important based on 4 statistics. exclusion of this variable decreased r square from 0.25 to 0.21. while gender was a useful variable in separation of patients into risk groups, its omission did n...