# Similarity Based Stratified Splitting: an approach to train better classifiers

arXiv: https://arxiv.org/abs/2010.06099 · HTML: https://mlsanity.com/paper/2010.06099/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Have we been Naive to Select Machine Learning Models? Noisy Data are here to Stay!](https://mlsanity.com/paper/2207.06651/) (2022)

## Abstract (arXiv)

We propose a Similarity-Based Stratified Splitting (SBSS) technique, which uses both the output and input space information to split the data. The splits are generated using similarity functions among samples to place similar samples in different splits. This approach allows for a better representation of the data in the training phase. This strategy leads to a more realistic performance estimation when used in real-world applications. We evaluate our proposal in twenty-two benchmark datasets with classifiers such as Multi-Layer Perceptron, Support Vector Machine, Random Forest and K-Nearest Neighbors, and five similarity functions Cityblock, Chebyshev, Cosine, Correlation, and Euclidean. According to the Wilcoxon Sign-Rank test, our approach consistently outperformed ordinary stratified 10-fold cross-validation in 75% of the assessed scenarios.