background & aim: in many medical studies, one data set is used to construct the model, and to test its performance. this approach is prone to over optimization, and leads to statistics with low chance of external validity. data splitting can be used to create training and test sets but the cost is reduction in power. the aim of this study was to demonstrate the ability of bootstrap aggregating...

this paper presents a data envelopment analysis (dea) model combined with bootstrapping to assess performance of one of the data mining algorithms. we applied a two-step process for performance productivity analysis of insurance branches within a case study. first, using a dea model, the study analyzes the productivity of eighteen decision-making units (dmus). using a malmquist index, dea deter...

Globalization increases the opportunities for unintentionally introduced invasive alien species, especially for insects, and most of these species could damage ecosystems and cause economic loss in China. In this study, we analyzed drivers of the distribution of unintentionally introduced invasive alien insects. Based on the number of unintentionally introduced invasive alien insects and their ...

We propose a new nonparametric learning method based on multivariate dyadic regression trees (MDRTs). Unlike traditional dyadic decision trees (DDTs) or classification and regression trees (CARTs), MDRTs are constructed using penalized empirical risk minimization with a novel sparsity-inducing penalty. Theoretically, we show that MDRTs can simultaneously adapt to the unknown sparsity and smooth...

Decision trees, either classification or regression trees, are especially attractive type of models for three main reasons. First, they have an intuitive representation, the resulting model is easy to understand and assimilate by humans [BFOS84]. Second, the decision trees are nonparametric models, no intervention being required from the user, and thus they are very suited for exploratory knowl...