Optimal feature selection for classifying a large set of chemicals using metal oxide sensors

Sensors and Actuators, B: Chemical

Volumn 187, Issue , 2013, Pages 471-480

  Nowotny, Thomas ^a,b   Berna, Amalia Z ^b   Binions, Russell ^c   Trowell, Stephen ^b  

^a UNIVERSITY OF SUSSEX   (United Kingdom)

^b CSIRO   (Australia)

^c QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Classification; Electronic nose; Feature selection; Metal oxide sensors; Support vector machines

Indexed keywords

CHROMIUM; ELECTRONIC NOSE; FEATURE EXTRACTION; METALLIC COMPOUNDS; METALS; SUPPORT VECTOR MACHINES; TIN; TIN OXIDES; TITANIUM; TITANIUM OXIDES; ZEOLITES;

CHROMIUM TITANIUM OXIDES; CLASSIFICATION PERFORMANCE; CLUSTERING PROPERTIES; FEATURE SELECTION PROBLEM; LINEAR SUPPORT VECTOR MACHINES; METAL OXIDE SENSORS; OPTIMAL FEATURE SELECTIONS; SENSOR TECHNOLOGIES;

CLASSIFICATION (OF INFORMATION);

EID: 84885474355     PISSN: 09254005     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.snb.2013.01.088     Document Type: Article

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