# Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks

arXiv: https://arxiv.org/abs/1811.01910 · HTML: https://mlsanity.com/paper/1811.01910/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 11 of 11)

- [Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification](https://mlsanity.com/paper/2109.05427/) (2021)
- [SVLTA: Benchmarking Vision-Language Temporal Alignment via Synthetic Video Situation](https://mlsanity.com/paper/2504.05925/) (2025)
- [Quantifying the Task-Specific Information in Text-Based Classifications](https://mlsanity.com/paper/2110.08931/) (2021)
- [What can we Learn by Predicting Accuracy?](https://mlsanity.com/paper/2208.01358/) (2022)
- [Small Language Models in the Real World: Insights from Industrial Text Classification](https://mlsanity.com/paper/2505.16078/) (2025)
- [Text Characterization Toolkit](https://mlsanity.com/paper/2210.01734/) (2022)
- [Identifying Semantically Difficult Samples to Improve Text Classification](https://mlsanity.com/paper/2302.06155/) (2023)
- [Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression models](https://mlsanity.com/paper/2401.10690/) (2024)
- [SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification](https://mlsanity.com/paper/2403.15574/) (2024)
- [Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models](https://mlsanity.com/paper/2502.08669/) (2025)
- [From Errors to Rules: Iterative Prompt Optimization for Text Classification](https://mlsanity.com/paper/2607.20497/) (2026)

## Abstract (arXiv)

Classification tasks are usually analysed and improved through new model architectures or hyperparameter optimisation but the underlying properties of datasets are discovered on an ad-hoc basis as errors occur. However, understanding the properties of the data is crucial in perfecting models. In this paper we analyse exactly which characteristics of a dataset best determine how difficult that dataset is for the task of text classification. We then propose an intuitive measure of difficulty for text classification datasets which is simple and fast to calculate. We show that this measure generalises to unseen data by comparing it to state-of-the-art datasets and results. This measure can be used to analyse the precise source of errors in a dataset and allows fast estimation of how difficult a dataset is to learn. We searched for this measure by training 12 classical and neural network based models on 78 real-world datasets, then use a genetic algorithm to discover the best measure of difficulty. Our difficulty-calculating code ( https://github.com/Wluper/edm ) and datasets ( http://data.wluper.com ) are publicly available.