Publications in Scientific Journals:
K. Varmuza, P. Filzmoser, M. Hilchenbach, H. Krüger, J. Silén:
"KNN classification - evaluated by repeated double cross validation: Recognition of minerals relevant for comet dust";
Chemometrics and Intelligent Laboratory Systems,
Volume 138
(2014),
15 November;
64
- 71.
English abstract:
Repeated double cross validation (rdCV) has recently been suggested as a careful and conservative strategy for optimizing and evaluating empirical multivariate calibration models. This evaluation strategy is adapted in this work for k-nearest neighbor (KNN) classification. The basics of rdCV are described, including the search for an optimum k, and tests with Italian Olive Oil Data. KNN-rdCV is applied to classify 17 mineral groups, relevant for the composition of comet dust particles, characterized by the peak heights at 20 selected masses in time-of-flight secondary ion mass spectra (TOF-SIMS). Predictive abilities for 15 mineral classes are > 95%, for two classes 75 and 85%.
Keywords:
Cross validation; Multicategory classification; Mass spectrometry; ROSETTA
"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)
http://dx.doi.org/10.1016/j.chemolab.2014.07.011
Electronic version of the publication:
http://www.sciencedirect.com/science/article/pii/S0169743914001580#
Created from the Publication Database of the Vienna University of Technology.