# Learning Penalty for Optimal Partitioning via Automatic Feature Extraction

arXiv: https://arxiv.org/abs/2505.07413 · HTML: https://mlsanity.com/paper/2505.07413/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 46 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 13 of 13)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Deep Lattice Networks and Partial Monotonic Functions](https://mlsanity.com/paper/1709.06680/) (2017)
- [Survival regression with accelerated failure time model in XGBoost](https://mlsanity.com/paper/2006.04920/) (2020)
- [ClaSP -- Parameter-free Time Series Segmentation](https://mlsanity.com/paper/2207.13987/) (2022)
- [On Optimal Multiple Changepoint Algorithms for Large Data](https://mlsanity.com/paper/1409.1842/) (2014)
- [Automatic Change-Point Detection in Time Series via Deep Learning](https://mlsanity.com/paper/2211.03860/) (2022)
- [Maximum Margin Interval Trees](https://mlsanity.com/paper/1710.04234/) (2017)
- [An Efficient Training Algorithm for Kernel Survival Support Vector Machines](https://mlsanity.com/paper/1611.07054/) (2016)
- [Labeled Optimal Partitioning](https://mlsanity.com/paper/2006.13967/) (2020)
- [A changepoint approach for the identification of financial extreme regimes](https://mlsanity.com/paper/1902.09205/) (2019)
- [Penalty Learning for Optimal Partitioning using Multilayer Perceptron](https://mlsanity.com/paper/2408.00856/) (2024)
- [Interval Regression: A Comparative Study with Proposed Models](https://mlsanity.com/paper/2503.02011/) (2025)

## Abstract (arXiv)

Changepoint detection identifies significant shifts in data sequences, making it important in areas like finance, genetics, and healthcare. The Optimal Partitioning algorithms efficiently detect these changes, using a penalty parameter to limit the changepoints count. Determining the optimal value for this penalty can be challenging. Traditionally, this process involved manually extracting statistical features, such as sequence length or variance to make the prediction. This study proposes a novel approach that uses recurrent networks to learn this penalty directly from raw sequences by automatically extracting features. Experiments conducted on 20 benchmark genomic datasets show that this novel method generally outperforms traditional ones in changepoint detection accuracy.