Resumo

GONZALEZ, Héctor; SANTOS, Gabriela; CAMPOS, Frank  e  MORELL PEREZ, Carlos. Evaluation of KNN-SP algorithm for multi-target prediction problems. Rev cuba cienc informat [online]. 2016, vol.10, n.3, pp.119-129. ISSN 2227-1899.

ABSTRACT This paper aims to develop a statistical evaluation of KNN algorithm for multi-target prediction problems. The cross-validation procedure is used to set the parameter and inverse distance as weigth to generate 4 variants of KNN for muti-target prediction. The study of statistical methods for comparison of multiple classifiers identified the non-parametric Friedman test as one of the most used in the testing of machine learning algorithms. In the experimental results, it employ 12 standards dataset and the metric aRRMSE. With application of the Nemenyi  post-hoc and Friedman tests showed that cross-validation procedure applied in IBKMTRCV and IBKMTRCVW is significantly better than the variants that do not use this procedure. The values of the average ranking of the Friedman test, show that IBKMTRCVW algorithm returns the best results.

Palavras-chave : machine learning; comparisons of multiple classifiers; KNN; Multi-target prediction.

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