NDLI: Random Forests, Nearest Shrunken Centroids and Support Vector Machines for the Classification of Diverse E-Nose Datasets

Content Provider	IEEE Xplore Digital Library
Author	Pardo, M. Sberveglieri, G.
Copyright Year	2006
Description	Author affiliation: CNR-INFM & Univ. of Brescia, Brescia (Pardo, M.; Sberveglieri, G.)
Abstract	Sensors practitioners don't make full use of the power of state-of-the-art pattern recognition (PR) algorithms and software. In this paper we apply -to our knowledge for the first time-Random Forests (RF) and Nearest Shrunken Centroids (NSC) to the classification of three E-Nose datasets of different hardness. We compare the classification rate with the one obtained by SVM. The classifiers parameters are optimized in an inner cross-validation (CV) cycle and the error is calculated by outer CV in order to avoid any bias. RF and SVM have a similar classification performance (SVM has an edge on the most difficult dataset). On the other hand, RF and NSC have an in-built feature selection mechanism that is very helpful for understanding the structure of the dataset and evaluating sensors.
Starting Page	424
Ending Page	426
File Size	759698
Page Count	3
File Format	PDF
ISBN	1424403758
ISSN	19300395
DOI	10.1109/ICSENS.2007.355496
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-22
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Support vector machine classification Radio frequency Sensor systems Data analysis Sensor phenomena and characterization Statistical analysis Error analysis Packaging Pattern recognition
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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