Monotone classification is a relatively recent topic in machine learning in which the classification function to learn is asked to guarantee a sort of monotonicity of the class with respect to attribute values. Nevertheless, real datasets are quite far from being monotone and this can sharply limit the performance of purely monotone classifiers while standard classifiers are simply insensitive to monotonicity. Here we focus on rank discrimination measures to be used in decision tree induction, i.e., functions able to measure the discrimination power of an attribute with respect to the class taking into account the monotonicity of the class with respect to the attribute. Three new measures are studied in detail and a hierarchical construction model is derived allowing the formal definition of a general rank discrimination measure. Our measures have been compared with other well-known proposals, quantifying both the accuracy and the monotonicity of the resulting binary decision tree classifiers.

Rank discrimination measures for enforcing monotonicity in decision tree induction

PETTURITI, DAVIDE
2015

Abstract

Monotone classification is a relatively recent topic in machine learning in which the classification function to learn is asked to guarantee a sort of monotonicity of the class with respect to attribute values. Nevertheless, real datasets are quite far from being monotone and this can sharply limit the performance of purely monotone classifiers while standard classifiers are simply insensitive to monotonicity. Here we focus on rank discrimination measures to be used in decision tree induction, i.e., functions able to measure the discrimination power of an attribute with respect to the class taking into account the monotonicity of the class with respect to the attribute. Three new measures are studied in detail and a hierarchical construction model is derived allowing the formal definition of a general rank discrimination measure. Our measures have been compared with other well-known proposals, quantifying both the accuracy and the monotonicity of the resulting binary decision tree classifiers.
2015
File in questo prodotto:
File Dimensione Formato  
POST-REV-ACCEPTED-MP-INFSCI-2015.pdf

accesso aperto

Descrizione: Post-print dell'autore
Tipologia di allegato: Post-print
Licenza: Creative commons
Dimensione 746.49 kB
Formato Adobe PDF
746.49 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1309103
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 26
  • ???jsp.display-item.citation.isi??? 21
social impact