M
M.C.P. de Souto
Researcher at University of Rio Grande
Publications - 28
Citations - 407
M.C.P. de Souto is an academic researcher from University of Rio Grande. The author has contributed to research in topics: Artificial neural network & Simulated annealing. The author has an hindex of 11, co-authored 28 publications receiving 372 citations. Previous affiliations of M.C.P. de Souto include Federal University of Pernambuco & Federal University of Rio Grande do Norte.
Papers
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Proceedings ArticleDOI
Ranking and selecting clustering algorithms using a meta-learning approach
M.C.P. de Souto,Ricardo B. C. Prudêncio,Rodrigo G. F. Soares,D.S.A. de Araujo,Ivan G. Costa,Teresa B. Ludermir,Alexander Schliep +6 more
TL;DR: A novel framework that applies a meta-learning approach to clustering algorithms that provides a ranking for the candidate algorithms that could be used with that dataset in the context of cancer gene expression micro-array datasets is presented.
Proceedings ArticleDOI
Using Accuracy and Diversity to Select Classifiers to Build Ensembles
TL;DR: A dynamic classifier selection (DCS) method is proposed, which takes into account both the accuracy and the diversity of the classifiers.
Proceedings ArticleDOI
Comparative study on normalization procedures for cluster analysis of gene expression datasets
M.C.P. de Souto,D.S.A. de Araujo,Ivan G. Costa,Rodrigo G. F. Soares,Teresa B. Ludermir,Alexander Schliep +5 more
TL;DR: A first large scale data driven comparative study of three normalization procedures applied to cancer gene expression data is presented in terms of the recovering of the true cluster structure as found by five different clustering algorithms.
Proceedings ArticleDOI
Optimization of neural network weights and architectures for odor recognition using simulated annealing
TL;DR: The algorithm generates networks with good generalization performance and low complexity for an odor recognition task in an artificial nose by using simulated annealing for optimizing neural network architectures and weights.
Journal ArticleDOI
Classification of vintages of wine by artificial nose using time delay neural networks
TL;DR: A pattern recognition system for an artificial nose that is composed of artificial neural networks with time delay taps on their inputs achieves better results than networks without delay taps for the classification of vintages of wine.