# Revisiting Data Complexity Metrics Based on Morphology for Overlap and Imbalance: Snapshot, New Overlap Number of Balls Metrics and Singular Problems Prospect

arXiv: https://arxiv.org/abs/2007.07935 · HTML: https://mlsanity.com/paper/2007.07935/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2020.

## Built on by (most cited first, 6 of 6)

- [Reducing Data Complexity using Autoencoders with Class-informed Loss Functions](https://mlsanity.com/paper/2111.06142/) (2021)
- [Multi-granularity Relabeled Under-sampling Algorithm for Imbalanced Data](https://mlsanity.com/paper/2201.03957/) (2022)
- [Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence](https://mlsanity.com/paper/2405.12326/) (2024)
- [iCost: A Novel Instance-Complexity-Based Cost-Sensitive Learning Framework](https://mlsanity.com/paper/2409.13007/) (2024)
- [Dataset Complexity Assessment Based on Cumulative Maximum Scaled Area Under Laplacian Spectrum](https://mlsanity.com/paper/2209.14743/) (2022)
- [Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction](https://mlsanity.com/paper/2407.14210/) (2024)

## Abstract (arXiv)

Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class prediction of new samples, learning from given training data. However, some properties can cause datasets to be problematic to classify. In order to evaluate a dataset a priori, data complexity metrics have been used extensively. They provide information regarding different intrinsic characteristics of the data, which serve to evaluate classifier compatibility and a course of action that improves performance. However, most complexity metrics focus on just one characteristic of the data, which can be insufficient to properly evaluate the dataset towards the classifiers' performance. In fact, class overlap, a very detrimental feature for the classification process (especially when imbalance among class labels is also present) is hard to assess. This research work focuses on revisiting complexity metrics based on data morphology. In accordance to their nature, the premise is that they provide both good estimates for class overlap, and great correlations with the classification performance. For that purpose, a novel family of metrics have been developed. Being based on ball coverage by classes, they are named after Overlap Number of Balls. Finally, some prospects for the adaptation of the former family of metrics to singular (more complex) problems are discussed.