title: Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).
abstract: The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks).
authors: Department of Chemistry, University of Rome "La Sapienza", P.le Aldo Moro 5, I-00185, Rome, Italy. Electronic address: marta.bevilacqua@uniroma1.it.
authors: Marta
authors: M
authors: Bevilacqua
authors: Department of Chemistry, University of Rome "La Sapienza", P.le Aldo Moro 5, I-00185, Rome, Italy.
authors: Federico
authors: F
authors: Marini
keywords: Distance-based weighting scheme; Locally weighted classification; Nearest neighbors; Non-linear classification; Partial least squares-discriminant analysis (PLS-DA)