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BACKGROUND The use of clustering methods for the discovery of cancer subtypes has drawn a great deal of attention in the scientific community. While bioinformaticians have proposed new clustering methods that take advantage of characteristics of the gene expression data, the medical community has a preference for using "classic" clustering methods. There(More)
Extreme Learning Machine (ELM) is a learning method for single-hidden layer feedforward neural network (SLFN) training. ELM approach increases the learning speed by means of randomly generating input weights and biases for hidden nodes rather than tuning network parameters, making this approach much faster than traditional gradient-based one. In this paper,(More)
This paper introduces a methodology for neural network global optimization. The aim is the simultaneous optimization of multilayer perceptron (MLP) network weights and architectures, in order to generate topologies with few connections and high classification performance for any data sets. The approach combines the advantages of simulated annealing, tabu(More)
We present here an original work that applies meta-learning approaches to select models for time-series forecasting. In our work, we investigated two meta-learning approaches, each one used in a diierent case study. Initially, we used a single machine learning algorithm to select among two models to forecast stationary time series (case study I). Following,(More)
Credit analysts generally assess the risk of credit applications based on their previous experience. They frequently employ quantitative methods to this end. Among the methods used, Artificial Neural Networks have been particularly successful and have been incorporated into several computational tools. However, the design of efficient Artificial Neural(More)
The optimization of architecture and weights of feed forward neural networks is a complex task of great importance in problems of supervised learning. In this work we analyze the use of the particle swarm optimization algorithm for the optimization of neural network architectures and weights aiming better generalization performances through the creation of(More)
– This paper shows results of using simulated annealing for optimizing neural network architectures and weights. The algorithm generates networks with good generalization performance (mean classification error of 5.28%) and low complexity (mean number of connections of 11.68 out of 36) for an odor recognition task in an artificial nose.
In this letter, the computational power of a class of random access memory (RAM)-based neural networks, called general single-layer sequential weightless neural networks (GSSWNNs), is analyzed. The theoretical results presented, besides helping the understanding of the temporal behavior of these networks, could also provide useful insights for the(More)
We present a novel framework that applies a meta-learning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algorithm selection task. In order to evaluate the framework(More)
The selection of a good model for forecasting a time series is a task that involves experience and knowledge. Employing machine learning algorithms is a promising approach to acquiring knowledge in regards to this task. A supervised classification method originating from the symbolic data analysis field is proposed for the model selection problem. This(More)