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Displaying 1-8 out of 8 total
GA Optimization of OBF TS Fuzzy Models with Linear and Non Linear Local Models
Found in: Neural Networks, Brazilian Symposium on
By Anderson V. Medeiros, Wagner C. Amaral, Ricardo J. G. B. Campello
Issue Date:October 2006
pp. 66-71
OBF (Orthonormal Basis Function) Fuzzy models have shown to be a promising approach to the areas of nonlinear system identification and control since they exhibit several advantages over those dynamic model topologies usually adopted in the literature. Alt...
 
A Comparative Study on the Use of Correlation Coefficients for Redundant Feature Elimination
Found in: Neural Networks, Brazilian Symposium on
By Pablo A. Jaskowiak, Ricardo J. G. B. Campello, Thiago F. Covões, Eduardo R. Hruschka
Issue Date:October 2010
pp. 13-18
Simplified Silhouette Filter (SSF) is a recently introduced feature selection method that automatically estimates the number of features to be selected. To do so, a sampling strategy is combined with a clustering algorithm that seeks clusters of correlated...
 
Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Pablo A. Jaskowiak,Ricardo J. G. B. Campello,Ivan G. Costa
Issue Date:July 2013
pp. 845-857
Cluster analysis is usually the first step adopted to unveil information from gene expression microarray data. Besides selecting a clustering algorithm, choosing an appropriate proximity measure (similarity or distance) is of great importance to achieve sa...
 
A Learning Object on Computational Intelligence
Found in: Advanced Learning Technologies, IEEE International Conference on
By Fábio A. P. Reis, Ígor C. Félix, Silvio L. Stanzani, Ricardo J. G. B. Campello, Leandro N. de Castro, Hermes Senger, Marta C. Rosatelli
Issue Date:July 2006
pp. 33
This paper presents a Learning Object in the domain of Computational Intelligence that can be used in graduate and undergraduate courses. Additionally, it can be reused in other contexts and scenarios, such as a distance learning course on Artificial Intel...
 
Evolutionary Algorithms for Clustering Gene-Expression Data
Found in: Data Mining, IEEE International Conference on
By Eduardo R. Hruschka, Leandro N. de Castro, Ricardo J. G. B. Campello
Issue Date:November 2004
pp. 403-406
This work deals with the problem of automatically finding optimal partitions in bioinformatics datasets. We propose incremental improvements for a Clustering Genetic Algorithm (CGA), culminating in the Evolutionary Algorithm for Clustering (EAC). The CGA a...
 
Proximity Measures for Clustering Gene Expression Microarray Data: A Validation Methodology and a Comparative Analysis
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Ivan G. Costa Filho, Pablo A. Jaskowiak, Ricardo J. G. B. Campello
Issue Date:July 2013
pp. 845-857
Cluster analysis is usually the first step adopted to unveil information from gene expression microarray data. Besides selecting a clustering algorithm, choosing an appropriate proximity measure (similarity or distance) is of great importance to achieve sa...
     
On the combination of relative clustering validity criteria
Found in: Proceedings of the 25th International Conference on Scientific and Statistical Database Management (SSDBM)
By Lucas Vendramin, Pablo A. Jaskowiak, Ricardo J. G. B. Campello
Issue Date:July 2013
pp. 1-12
Many different relative clustering validity criteria exist that are very useful as quantitative measures for assessing the quality of data partitions. These criteria are endowed with particular features that may make each of them more suitable for specific...
     
A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Davoud Moulavi, Joerg Sander, Ricardo J. G. B. Campello
Issue Date:November 2012
pp. 1850-1852
In [CHECK END OF SENTENCE], the authors proposed a framework for automated clustering and visualization of biological data sets named AUTO-HDS. This letter is intended to complement that framework by showing that it is possible to get rid of a user-defined...
     
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