The Preference Learning Toolbox

TL;DR

The paper presents an open source preference learning toolbox designed to facilitate data preprocessing, feature selection, and learning, addressing the growing importance of ordinal data in machine learning.

Contribution

It introduces a scalable, efficient, and accessible toolbox supporting key preference learning tasks, enhancing research and practical applications.

Findings

Supports various preference learning methods

Includes data preprocessing and feature selection tools

Facilitates scalable and efficient preference learning

Abstract

Preference learning (PL) is a core area of machine learning that handles datasets with ordinal relations. As the number of generated data of ordinal nature is increasing, the importance and role of the PL field becomes central within machine learning research and practice. This paper introduces an open source, scalable, efficient and accessible preference learning toolbox that supports the key phases of the data training process incorporating various popular data preprocessing, feature selection and preference learning methods.