Multistage tree model for crime dataset in Iraq

Abstract EN

This research deals with the using of correlation measurement that leads to describing the degree of relationship between variables, quantities or qualities.

therefore, we implement a simple correlation coefficient and conditional correlation to introduce a regular vine copula, which gives different tree structures.

two methods to select tree structures are introduced.

the first one adopts the partial correlation constant (PCC) with constant, while the second method depends on the estimation of summation pathway.

the proposed method makes modification on diβmann’s algorithm to increase the dependency on each level of the tree using rank correlation measurement.

both methods are adopted to construct the best model with more than three dimensions based on the available label crime dataset in Iraq.

the selected model is used for selecting the suitable tree model and generating a decision with the low dimensionality of variables.