Improving an Ensemble of Neural Networks via a Novel Multi-class Decomposition Schema

Antonio Alfeo, Antonio Alfeo, Mario Cimino, Mario Cimino, Guido Gagliardi, Guido Gagliardi, Guido Gagliardi

2022

Abstract

The need for high recognition performance demands increasingly complex machine learning (ML) architec-tures, which might be extremely computationally burdensome to be implemented in real-world. This issue can be addressed by using an ensemble learning model to decompose the multi-class classification problem into many simpler binary classification problems, e.g. each binary classification problem can be handled via a simple multi-layer perceptron (MLP). The so-called one-versus-one (OVO) is a widely used multi-class decomposition schema in which each classifier is trained to distinguish between two classes. However, with an OVO schema each MLP is non-competent to classify instances of classes that have not been used to train it. This results in classification noise that may degrade the performance of the whole ensemble, especially when the number of classes grows. The proposed architecture employs a weighting mechanism to minimize the contribution of the non-competent MLPs and combine their outcomes to effectively solve the multi-class classification problem. In this work, the robustness to the classification noise introduced by non-competent MLPs is measured to assess in what conditions this translates in better classification accuracy. We test the proposed approach with five different benchmark data sets, outperforming both the baseline and one state-of-the-art approach in multi-class decomposition algorithms.

Paper Citation

in Harvard Style

Alfeo A., Cimino M. and Gagliardi G. (2022). Improving an Ensemble of Neural Networks via a Novel Multi-class Decomposition Schema. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA; ISBN 978-989-758-611-8, SciTePress, pages 389-395. DOI: 10.5220/0011547900003332

in Bibtex Style

@conference{ncta22,
author={Antonio Alfeo and Mario Cimino and Guido Gagliardi},
title={Improving an Ensemble of Neural Networks via a Novel Multi-class Decomposition Schema},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA},
year={2022},
pages={389-395},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011547900003332},
isbn={978-989-758-611-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA
TI - Improving an Ensemble of Neural Networks via a Novel Multi-class Decomposition Schema
SN - 978-989-758-611-8
AU - Alfeo A.
AU - Cimino M.
AU - Gagliardi G.
PY - 2022
SP - 389
EP - 395
DO - 10.5220/0011547900003332
PB - SciTePress