Fine-tuning of the SOMkNN classifier

Silva L.A.; Kitani E.C.; Del-Moral-Hernandez E.

Fine-tuning of the SOMkNN classifier

Tipo

Artigo de evento

Data de publicação

2013

Periódico

Proceedings of the International Joint Conference on Neural Networks

Citações (Scopus)

2

Autores

Silva L.A.
Kitani E.C.
Del-Moral-Hernandez E.

Resumo

Classification is an important data mining task used in decision-making processes. Techniques such as Artificial Neural Networks (ANN) and Statistics are used to help in an automatic classification. In a previous work, we proposed a method for classification problems based on Self-Organizing Maps ANN (SOM) and k Nearest Neighbor (kNN) statistical classifier. The SOMkNN classifier, as we call this combination, is much faster than the traditional kNN and it keeps equivalent rates results. We propose a fine-tuning for this classifier here, which consists of a neuron relocation of the SOM map. The experiments presented compare SOMkNN with and without fine-tuning. Experiments using 8 databases, 6 of which are available in the UCI repository, the fine-tuning results are an improvement classification rate in 7 databases and in the last one the result is the same. The results indicate a trend of classification rate improvement with the application of the fine tuning technique. The gain in rate is approximately 1.2% and experiments were performed in order to correlate the results. © 2013 IEEE.

Assuntos Scopus

Automatic classification , Classification rates , Data mining tasks , Decision making process , K nearest neighbor (KNN) , Statistical classifier , UCI repository , Without fine-tuning