TY - GEN
T1 - Classification Through Graphical Models
T2 - 16th Conference of the International Federation of Classification Societies, IFCS 2019
AU - Nicolussi, Federica
AU - Di Brisco, Agnese Maria
AU - Cazzaro, Manuela
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - The purpose of this work is to evaluate the level of perceived health by studying possible factors such as personal information, economic status, and use of free time. The analysis is carried out on the European Union Statistics on Income and Living Conditions (EU-SILC) survey covering 31 European countries. At this aim, we take advantage of graphical models that are suitable tools to represent complex dependence structures among a set of variables. In particular, we consider a special case of Chain Graph model, known as Chain Graph models of type IV for categorical variables. We implement a Bayesian learning procedure to discover the graph which best represents the dataset. Finally, we perform a classification algorithm based on classification trees to identify clusters.
AB - The purpose of this work is to evaluate the level of perceived health by studying possible factors such as personal information, economic status, and use of free time. The analysis is carried out on the European Union Statistics on Income and Living Conditions (EU-SILC) survey covering 31 European countries. At this aim, we take advantage of graphical models that are suitable tools to represent complex dependence structures among a set of variables. In particular, we consider a special case of Chain Graph model, known as Chain Graph models of type IV for categorical variables. We implement a Bayesian learning procedure to discover the graph which best represents the dataset. Finally, we perform a classification algorithm based on classification trees to identify clusters.
KW - Bayesian learning procedure
KW - Chain regression graph models
KW - Perceived health
UR - https://www.scopus.com/pages/publications/85102727349
U2 - 10.1007/978-3-030-60104-1_22
DO - 10.1007/978-3-030-60104-1_22
M3 - Conference contribution
SN - 9783030601034
T3 - Studies in Classification, Data Analysis, and Knowledge Organization
SP - 197
EP - 204
BT - Data Analysis and Rationality in a Complex World
A2 - Chadjipadelis, Theodore
A2 - Lausen, Berthold
A2 - Markos, Angelos
A2 - Lee, Tae Rim
A2 - Montanari, Angela
A2 - Nugent, Rebecca
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 August 2019 through 29 August 2019
ER -