We here introduce a novel classification approach adopted from the nonlinear model identification framework, which jointly addresses the feature selection (FS) and classifier design tasks. The classifier is constructed as a polynomial expansion of the original features and a selection process is applied to find the relevant model terms. The selection method progressively refines a probability distribution defined on the model structure space, by extracting sample models from the current distribution and using the aggregate information obtained from the evaluation of the population of models to reinforce the probability of extracting the most important terms. To reduce the initial search space, distance correlation filtering is optionally applied as a preprocessing technique. The proposed method is compared to other well-known FS and classification methods on standard benchmark problems. Besides the favorable properties of the method regarding classification accuracy, the obtained models have a simple structure, easily amenable to interpretation and analysis.

A Feature Selection and Classification Algorithm Based on Randomized Extraction of Model Populations

BRANKOVIC, AIDA;FALSONE, ALESSANDRO;PRANDINI, MARIA;PIRODDI, LUIGI
2018-01-01

Abstract

We here introduce a novel classification approach adopted from the nonlinear model identification framework, which jointly addresses the feature selection (FS) and classifier design tasks. The classifier is constructed as a polynomial expansion of the original features and a selection process is applied to find the relevant model terms. The selection method progressively refines a probability distribution defined on the model structure space, by extracting sample models from the current distribution and using the aggregate information obtained from the evaluation of the population of models to reinforce the probability of extracting the most important terms. To reduce the initial search space, distance correlation filtering is optionally applied as a preprocessing technique. The proposed method is compared to other well-known FS and classification methods on standard benchmark problems. Besides the favorable properties of the method regarding classification accuracy, the obtained models have a simple structure, easily amenable to interpretation and analysis.
2018
Control and Systems Engineering; Software; Information Systems; Human-Computer Interaction; Computer Science Applications1707 Computer Vision and Pattern Recognition; Electrical and Electronic Engineering
File in questo prodotto:
File Dimensione Formato  
RFSC_final.pdf

accesso aperto

Descrizione: BrankovicFalsonePrandiniPiroddi2017
: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 397.16 kB
Formato Adobe PDF
397.16 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/11311/1027725
Citazioni
  • ???jsp.display-item.citation.pmc??? 2
  • Scopus 24
  • ???jsp.display-item.citation.isi??? 19
social impact