KNN classification — evaluated by repeated double cross validation: Recognition of minerals relevant for comet dust

• We combine “repeated double cross validation” (rdCV) with KNN classification.
• rdCV estimates the optimum no. of neighbors in KNN, independent from evaluation.
• rdCV gives cautious estimations of predictive abilities (and their variabilities).
• We apply KNN-rdCV to classify the origin of Italian olive oils.
• We apply KNN-rdCV to classify minerals relevant in comet dust particles (ROSETTA).

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%.