CINECA IRIS Institutional Research Information System
IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.
EnglishAccording to a recent trend of research, there is a growing interest in applications of machine learning techniques to business analytics. In this work, both supervised and unsupervised machine learning techniques are applied to the analysis of a dataset made of both family and non-family firms. This is worth investigating, because the two kinds of firms typically differ in some aspects related to performance, which can be reflected in balance sheet data. First, binary classification techniques are applied to discriminate the two kinds of firms, by combining an unlabeled dataset with the labels provided by a survey. The most important features for performing such binary classification are identified. Then, clustering is applied to highlight why supervised learning can be effective in the previous task, by showing that most of the largest clusters found are quite unequally populated by the two classes.
| Lingua: | Inglese |
| Titolo: | Machine Learning Application to Family Business Status Classification |
| Autori: | |
| Data di pubblicazione: | 2020 |
| Autori: | Gnecco, G.; Amato, S.; Patuelli, A.; Lattanzi, N. |
| Presenza coautori internazionali: | No |
| Numero degli autori: | 4 |
| Titolo del libro: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Serie: | LECTURE NOTES IN COMPUTER SCIENCE |
| Nome del convegno: | 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020 |
| Volume: | 12565 |
| Pagina iniziale: | 25 |
| Pagina finale: | 36 |
| Numero di pagine: | 12 |
| ISBN: | 978-3-030-64582-3 978-3-030-64583-0 |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1007/978-3-030-64583-0_3 |
| Editore: | Springer Science and Business Media Deutschland GmbH |
| Anno del convegno: | 2020 |
| Luogo del convegno: | ita |
| Codice identificativo Scopus: | 2-s2.0-85101223324 |
| Abstract: | According to a recent trend of research, there is a growing interest in applications of machine learning techniques to business analytics. In this work, both supervised and unsupervised machine learning techniques are applied to the analysis of a dataset made of both family and non-family firms. This is worth investigating, because the two kinds of firms typically differ in some aspects related to performance, which can be reflected in balance sheet data. First, binary classification techniques are applied to discriminate the two kinds of firms, by combining an unlabeled dataset with the labels provided by a survey. The most important features for performing such binary classification are identified. Then, clustering is applied to highlight why supervised learning can be effective in the previous task, by showing that most of the largest clusters found are quite unequally populated by the two classes. |
| Parole Chiave: | Machine learning Clustering Supervised learning Family business |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
File in questo prodotto:
| File | Descrizione | Tipologia | Licenza | |
|---|---|---|---|---|
| Gnecco et al. 2020.pdf | Articolo | N/A | Nessuna licenza | Administrator Richiedi una copia |
http://hdl.handle.net/20.500.11771/18197
Copyright © 2021