# Meta-Learning for Resampling Recommendation Systems

## TL;DR

This paper proposes a meta-learning approach to recommend resampling methods for imbalanced classification datasets, aiming to improve classification quality without exhaustive search.

## Contribution

It introduces a novel meta-learning framework for selecting resampling techniques based on dataset properties, addressing the resampling selection problem efficiently.

## Key findings

- Meta-learning effectively recommends resampling methods.
- Improves classification performance on imbalanced datasets.
- Reduces computational cost compared to exhaustive search.

## Abstract

One possible approach to tackle the class imbalance in classification tasks is to resample a training dataset, i.e., to drop some of its elements or to synthesize new ones. There exist several widely-used resampling methods. Recent research showed that the choice of resampling method significantly affects the quality of classification, which raises resampling selection problem. Exhaustive search for optimal resampling is time-consuming and hence it is of limited use. In this paper, we describe an alternative approach to the resampling selection. We follow the meta-learning concept to build resampling recommendation systems, i.e., algorithms recommending resampling for datasets on the basis of their properties.