SplitSoftening: Package: Softening splits in classification trees

In SplitSoftening: Softening Splits in Decision Trees

Description

The basic idea of split softening is to modify the process of classification of an input case with a decision tree such that in the area near the threshold of a softened split both branches of the tree are used to provide a prediction for the submitted case and their results are combined.

Details

Functions in this package allow to add softening to the nodes of a classification tree created with the package tree. Each node where a decision on a continuous variable is made is enriched with softening parameters which specify the boundaries of the softening area and which together with the original split threshold determine the weights of the branches when combined.

The weights of branches are (1/2, 1/2) in the original split threshold. Other points inside the softening area have weights given by linear interpolation to reach the values (0, 1), or vice versa, on the boundaries of the softening area.

A data structure for a decision tree prepared for softening can be created from a tree object with the softsplits function.

Softening parameters may be set to the ‘soft tree’ structure. The package offers the following functions for this purpose:

• softening.by.data.range

• softening.by.esd

• softening.optimized

• soften

A softened tree might be used to obtain a prediction for a dataset using the predictSoftsplits function.

References

Dvořák, J. (2019), Classification trees with soft splits optimized for ranking <doi:10.1007/s00180-019-00867-1> https://rdcu.be/bkeW2

SplitSoftening documentation built on Oct. 8, 2021, 5:07 p.m.