Construction de variables a l'aide de classifieurs comme aide a la regression

TL;DR

This paper introduces a generic pre-processing method that automatically creates additional variables using classifiers to improve regression performance by enriching the input data.

Contribution

It presents a novel approach for variable creation in regression tasks by discretizing target values and training classifiers to generate supplementary features.

Findings

Enrichment improves regression accuracy across multiple datasets.

Method is effective with various types of regressors.

Validated on 33 datasets with positive results.

Abstract

This paper proposes a method for the automatic creation of variables (in the case of regression) that complement the information contained in the initial input vector. The method works as a pre-processing step in which the continuous values of the variable to be regressed are discretized into a set of intervals which are then used to define value thresholds. Then classifiers are trained to predict whether the value to be regressed is less than or equal to each of these thresholds. The different outputs of the classifiers are then concatenated in the form of an additional vector of variables that enriches the initial vector of the regression problem. The implemented system can thus be considered as a generic pre-processing tool. We tested the proposed enrichment method with 5 types of regressors and evaluated it in 33 regression datasets. Our experimental results confirm the interest of the approach.