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Pattern classification has been successfully applied in many problem domains, such as biometric recognition , document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from(More)
Brain Computer Interface systems (BCIs) based on Electroencephalogram (EEG) signal processing allow to translate the subject's brain activities into control commands for computer devices. This paper presents an efficient embedded approach for feature selection and linear discrimination of EEG signals. In the first stage, four well-known feature extraction(More)
OBJECTIVES Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. This work evaluates the performance of several statistical and machine learning imputation methods that were used to predict recurrence in patients in an extensive real breast cancer data set. (More)
Discriminative features have to be properly extracted and selected from the electroencephalographic (EEG) signals of each specific subject in order to achieve an adaptive brain-computer interface (BCI) system. This work presents an efficient wrapper-based methodology for feature selection and least squares discrimination of high-dimensional EEG data with(More)
Missing data is a common drawback in many real-life pattern classification scenarios. One of the most popular solutions is missing data imputation by the K nearest neighbours ðKNNÞ algorithm. In this article, we propose a novel KNN imputation procedure using a feature-weighted distance metric based on mutual information (MI). This method provides a missing(More)
Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and(More)
Extracting knowledge from electroencephalographic (EEG) signals has become an increasingly important research area in biomedical engineering. In addition to its clinical diagnostic purposes, in recent years there have been many efforts to develop brain computer interface (BCI) systems, which allow users to control external devices only by using their brain(More)
—Multi-Layer Perceptrons (MLPs) have been successfully applied in many pattern classification tasks. However, a drawback of these learning machines is that they cannot handle input vectors that present missing data on its features. A recommended way for dealing with missing values is imputation, i.e., to fill in missing data with plausible values. This(More)