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

TL;DR

This paper introduces a new framework for multi-class classification using imprecise, fuzzy features, providing theoretical analysis and practical algorithms based on SVM and neural networks, validated by experiments.

Contribution

It proposes the first theoretical framework and algorithms for multi-class classification with fuzzy features, addressing a realistic problem with imprecise observations.

Findings

Theoretical analysis confirms the validity of the fuzzy Rademacher complexity approach.

Algorithms based on SVM and neural networks effectively handle fuzzy features.

Experimental results demonstrate the practicality and accuracy of the proposed methods.

Abstract

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.