# Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms

arXiv: https://arxiv.org/abs/2206.04311 · HTML: https://mlsanity.com/paper/2206.04311/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 70 references that are in the map.
- First posted on arXiv 9 Jun 2022.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Train faster, generalize better: Stability of stochastic gradient descent](https://mlsanity.com/paper/1509.01240/) (2015)
- [Learning Deep Kernels for Non-Parametric Two-Sample Tests](https://mlsanity.com/paper/2002.09116/) (2020)
- [Optimal Learners for Multiclass Problems](https://mlsanity.com/paper/1405.2420/) (2014)
- [Open Set Domain Adaptation: Theoretical Bound and Algorithm](https://mlsanity.com/paper/1907.08375/) (2019)
- [A PAC-Bayesian Tutorial with A Dropout Bound](https://mlsanity.com/paper/1307.2118/) (2013)
- [Oversampling for Imbalanced Learning Based on K-Means and SMOTE](https://mlsanity.com/paper/1711.00837/) (2017)
- [Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data](https://mlsanity.com/paper/2106.07636/) (2021)
- [How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?](https://mlsanity.com/paper/2101.01104/) (2020)
- [Rademacher Complexity Bounds for a Penalized Multiclass Semi-Supervised Algorithm](https://mlsanity.com/paper/1607.00567/) (2016)
- [Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data](https://mlsanity.com/paper/1810.13404/) (2018)
- [Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation](https://mlsanity.com/paper/2007.14612/) (2020)
- [Local Rademacher Complexity for Multi-label Learning](https://mlsanity.com/paper/1410.6990/) (2014)

## Built on by (most cited first, 1 of 1)

- [Interval Type-2 Fuzzy Neural Networks for Multi-Label Classification](https://mlsanity.com/paper/2302.10430/) (2023)

## Abstract (arXiv)

The theoretical analysis of multi-class classification has proved that the existing multi-class classification methods can train a classifier with high classification accuracy on the test set, when the instances are precise in the training and test sets with same distribution and enough instances can be collected in the training set. However, one limitation with multi-class classification has not been solved: how to improve the classification accuracy of multi-class classification problems when only imprecise observations are available. Hence, in this paper, we propose a novel framework to address a new realistic problem called multi-class classification with imprecise observations (MCIMO), where we need to train a classifier with fuzzy-feature observations. Firstly, we give the theoretical analysis of the MCIMO problem based on fuzzy Rademacher complexity. Then, two practical algorithms based on support vector machine and neural networks are constructed to solve the proposed new problem. Experiments on both synthetic and real-world datasets verify the rationality of our theoretical analysis and the efficacy of the proposed algorithms.