# Affinity-Based Hierarchical Learning of Dependent Concepts for Human Activity Recognition

arXiv: https://arxiv.org/abs/2104.04889 · HTML: https://mlsanity.com/paper/2104.04889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 11 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks](https://mlsanity.com/paper/1903.05987/) (2019)
- [Hierarchically Structured Meta-learning](https://mlsanity.com/paper/1905.05301/) (2019)
- [Evaluation Measures for Hierarchical Classification: a unified view and novel approaches](https://mlsanity.com/paper/1306.6802/) (2013)
- [Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity](https://mlsanity.com/paper/1810.05596/) (2018)
- [Impact of Ground Truth Annotation Quality on Performance of Semantic Image Segmentation of Traffic Conditions](https://mlsanity.com/paper/1901.00001/) (2018)

## Abstract (arXiv)

In multi-class classification tasks, like human activity recognition, it is often assumed that classes are separable. In real applications, this assumption becomes strong and generates inconsistencies. Besides, the most commonly used approach is to learn classes one-by-one against the others. This computational simplification principle introduces strong inductive biases on the learned theories. In fact, the natural connections among some classes, and not others, deserve to be taken into account. In this paper, we show that the organization of overlapping classes (multiple inheritances) into hierarchies considerably improves classification performances. This is particularly true in the case of activity recognition tasks featured in the SHL dataset. After theoretically showing the exponential complexity of possible class hierarchies, we propose an approach based on transfer affinity among the classes to determine an optimal hierarchy for the learning process. Extensive experiments show improved performances and a reduction in the number of examples needed to learn.