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Classification Based Hierarchical Clustering Prediction Variability in the Ensembles of Models Using a Statistical Approach

A diversity of model decisions in ensembles is the basis for using group decision-making methods. However, creating a diversity of individual decisions and using them to create an ensemble is not easy. It is also not easy to develop a method of forming a decision based on a diversity of opinions. Often, decisions strongly correlate and the individual opinion of a particular model is little taken into account in the general decision of the ensemble. Similar solutions are often grouped and outweigh the solutions that differ from the group in their total number. In this regard, a hierarchical classification with clustering based on the features of the correlation of decisions is proposed. This allowing to aggregate strongly similar opinions and to a greater extent take into account the alternative opinion, at the next level of the hierarchy. At the same time, conditions are formed for taking into account the maximum diversity of local decisions with a general unbiased assessment of ensemble decisions. Clustering a decision based on the features of correlation allowing to replace a group of similar decisions with a general cluster decision. The hierarchical structure allows dividing decisions into local and global beyond the hierarchy level.