Autor(es): Affonso, Carlos [UNESP] ; Sassi, Renato Jose ; Barreiros, Ricardo Marques [UNESP]
Data: 2018
Identificador Persistente: http://hdl.handle.net/11449/177504
Origem: Oasisbr
Assunto(s): Artificial neural network; Feature selection; Fuzzy sets; Image identification; Rough sets; Artificial neural network; Artificial neural network; Feature selection; Feature selection; Fuzzy sets; Fuzzy sets; Image identification; Image identification; Rough sets; Rough sets
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This paper presents a methodology to biological image classification through a Rough-Fuzzy Artificial Neural Network (RFANN). This approach is used in order to improve the learning process by Rough Sets Theory (RS) focusing on the feature selection, considering that the RS feature selection allows the use of low dimension features from the image database. This result could be achieved, once the image features are characterized using membership functions and reduced it by Fuzzy Sets rules. The RS identifies the attributes relevance and the Fuzzy relations influence on the Artificial Neural Network (ANN) surface response. Thus, the features filtered by Rough Sets are used to train a Multilayer Perceptron Neuro Fuzzy Network. The reduction of feature sets reduces the complexity of the neural network structure therefore improves its runtime. To measure the performance of the proposed RFANN the runtime and training error were compared to the unreduced features.
Department of EIM, Universidade Julio de Mesquita Filho, UNESP
Industrial Engineering Post Graduation, Universidade Nove de Julho, UNINOVE
Department of EIM, Universidade Julio de Mesquita Filho, UNESP
