Prototype Selection Using Self-Organizing-Maps and Entropy for Overlapped Classes and Imbalanced Data

Rubbo M.; Silva L.A.

Prototype Selection Using Self-Organizing-Maps and Entropy for Overlapped Classes and Imbalanced Data

Tipo

Artigo de evento

Data de publicação

2018

Periódico

Proceedings of the International Joint Conference on Neural Networks

Citações (Scopus)

2

Autores

Rubbo M.
Silva L.A.

Resumo

© 2018 IEEE.The k nearest neighbor kNN is a traditional supervised classifier used in data mining tasks. However, when used in real applications, mainly in a dataset with class imbalance or class overlap, kNN suffers with problems in accuracy performance. In this paper, we propose three prototype selection methods using self-organizing maps (SOM) and information entropy to increase the effectiveness of the kNN classifier in datasets with these conditions. The methods, named SOMEntropyKnn, were able to increase the effectiveness of the kNN classifier in all the 14 datasets used in the experiment, increasing the accuracy performance from datasets with imbalance or overlap problems.

Assuntos Scopus

class overlap , Data mining tasks , Imbalanced data , Information entropy , K-nearest neighbors , Prototype selection , Real applications , Supervised classifiers