# Minimal Learning Machine for Multi-Label Learning

arXiv: https://arxiv.org/abs/2305.05518 · HTML: https://mlsanity.com/paper/2305.05518/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 120 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 15 of 15)

- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Comprehensive Comparative Study of Multi-Label Classification Methods](https://mlsanity.com/paper/2102.07113/) (2021)
- [Deep Extreme Multi-label Learning](https://mlsanity.com/paper/1704.03718/) (2017)
- [Classifier Chains: A Review and Perspectives](https://mlsanity.com/paper/1912.13405/) (2019)
- [ML-Net: multi-label classification of biomedical texts with deep neural networks](https://mlsanity.com/paper/1811.05475/) (2018)
- [Global optimization via inverse distance weighting and radial basis functions](https://mlsanity.com/paper/1906.06498/) (2019)
- [Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations](https://mlsanity.com/paper/2204.09781/) (2022)
- [A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation](https://mlsanity.com/paper/1912.08976/) (2019)
- [Learning Gradient Boosted Multi-label Classification Rules](https://mlsanity.com/paper/2006.13346/) (2020)
- [Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification](https://mlsanity.com/paper/1911.01658/) (2019)
- [Deep Hiearchical Multi-Label Classification Applied to Chest X-Ray Abnormality Taxonomies](https://mlsanity.com/paper/2009.05609/) (2020)
- [Ensemble Methods for Multi-label Classification](https://mlsanity.com/paper/1307.1769/) (2013)
- [Group Preserving Label Embedding for Multi-Label Classification](https://mlsanity.com/paper/1812.09910/) (2018)
- [Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection](https://mlsanity.com/paper/1909.09978/) (2019)
- [Dynamic classifier chains for multi-label learning](https://mlsanity.com/paper/1710.07491/) (2017)

## Abstract (arXiv)

Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose new methods and evaluate how their core component, the distance mapping, can be adapted to multi-label learning. The proposed approach is based on combining the distance mapping with an inverse distance weighting. Although the proposal is one of the simplest methods in the multi-label learning literature, it achieves state-of-the-art performance for small to moderate-sized multi-label learning problems. In addition to its simplicity, the proposed method is fully deterministic: Its hyper-parameter can be selected via ranking loss-based statistic which has a closed form, thus avoiding conventional cross-validation-based hyper-parameter tuning. In addition, due to its simple linear distance mapping-based construction, we demonstrate that the proposed method can assess the uncertainty of the predictions for multi-label classification, which is a valuable capability for data-centric machine learning pipelines.