Fine-tuning of the SOMkNN classifier

dc.contributor.authorSilva L.A.
dc.contributor.authorKitani E.C.
dc.contributor.authorDel-Moral-Hernandez E.
dc.description.abstractClassification 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.
dc.relation.ispartofProceedings of the International Joint Conference on Neural Networks
dc.rightsAcesso Restrito
dc.titleFine-tuning of the SOMkNN classifier
dc.typeArtigo de evento
local.scopus.subjectAutomatic classification
local.scopus.subjectClassification rates
local.scopus.subjectData mining tasks
local.scopus.subjectDecision making process
local.scopus.subjectK nearest neighbor (KNN)
local.scopus.subjectStatistical classifier
local.scopus.subjectUCI repository
local.scopus.subjectWithout fine-tuning