Supervised feature evaluation by consistency analysis: application to measure sets used to characterise geographic objects

TL;DR

This paper introduces a method to evaluate feature sets in supervised learning by analyzing the consistency of example bases, demonstrated through a geomatic case study on geographic object characterization.

Contribution

It presents a novel consistency-based evaluation method for feature sets, specifically applied to geographic data in a supervised learning context.

Findings

The method provides relevant evaluations of measure sets.

It effectively assesses feature set quality in geographic object characterization.

The case study validates the usefulness of the proposed approach.

Abstract

Nowadays, supervised learning is commonly used in many domains. Indeed, many works propose to learn new knowledge from examples that translate the expected behaviour of the considered system. A key issue of supervised learning concerns the description language used to represent the examples. In this paper, we propose a method to evaluate the feature set used to describe them. Our method is based on the computation of the consistency of the example base. We carried out a case study in the domain of geomatic in order to evaluate the sets of measures used to characterise geographic objects. The case study shows that our method allows to give relevant evaluations of measure sets.