Hierarchical kernel applied to mixture model for the classification of binary predictors

Seydou Nourou Sylla (1, 2, 3) , Stéphane Girard (4) , Abdou Kâ Diongue (3) , Aldiouma Diallo (5) , Cheikh Sokhna (5)

1 URMITE - Unité de Recherche sur les Maladies Infectieuses Tropicales Emergentes
2 LIRIMA - Laboratoire International de Recherche en Informatique et Mathématiques Appliquées
3 LERSTAD - laboratoire d'Etudes et de recherches en Statistiques et Développement
4 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
5 URMITE - Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes

Diagnosis systems often use structured data. These data have a hierarchical structure related with the questions asked during the interview with the doctor or the survey taker in charge of verbal autopsies. The hierarchical nature of these questions leads to consider this aspect when analyzing medical data. Thus, it is recommendable to choose a similarity measure that takes into account this issue to better represent the reality. We propose the introduction of a kernel taking into account the hierarchical structure of the data interactions between sub-items in supervised binary classification methods. This kernel can integrate the knowledge from the application domain relative to how the features of the problem are organized. We focus on problems whose features can be hierarchically structured. These hierarchies are represented by trees on two levels. Our main contribution is the proposal of a kernel that simultaneously takes into account the hierarchical appearance and the interaction between variables. The proposed kernel has shown a good classification performance on a complex set of medical data including a high number of predictors and classes.

Mots clés: hierarchical kernel; Diagnosis systems; classification; similarity

Domaines: Statistiques [math.ST]

Seydou Nourou Sylla, Stéphane Girard, Abdou Kâ Diongue, Aldiouma Diallo, Cheikh Sokhna. Hierarchical kernel applied to mixture model for the classification of binary predictors. 61st ISI World Statistics Congress, Jul 2017, Marrakech, Morocco. ⟨hal-01587163⟩