Navegando por Palavras-chave "Feature selection"

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    Feature selection before EEG classification supports the diagnosis of Alzheimer's disease
    (Elsevier Ireland Ltd, 2017) Trambaiolli, L. R.; Spolaor, N.; Lorena, A. C. [UNIFESP]; Anghinah, R.; Sato, J. R.
    Objective: In many decision support systems, some input features can be marginal or irrelevant to the diagnosis, while others can be redundant among each other. Thus, feature selection (FS) algorithms are often considered to find relevant/non-redundant features.& para;& para;Objective: This study aimed to evaluate the relevance of FS approaches applied to Alzheimer's Disease (AD) EEG-based diagnosis and compare the selected features with previous clinical findings.& para;& para;Methods: Eight different FS algorithms were applied to EEG spectral measures from 22 AD patients and 12 healthy age-matched controls. The FS contribution was evaluated by considering the leave-one-subject-out accuracy of Support Vector Machine classifiers built in the datasets described by the selected features.& para;& para;Results: The Filtered Subset Evaluator technique achieved the best performance improvement both on a per-patient basis (91.18% of accuracy) and on a per-epoch basis (85.29 +/- 21.62%), after removing 88.76 +/- 1.12% of the original features. All algorithms found out that alpha and beta bands are relevant features, which is in agreement with previous findings from the literature.& para;& para;Conclusion: Biologically plausible EEG datasets could achieve improved accuracies with pre-processing FS steps.& para;& para;Significance: The results suggest that the FS and classification techniques are an attractive complementary tool in order to reveal potential biomarkers aiding the AD clinical diagnosis. (C) 2017 Published by Elsevier Ireland Ltd on behalf of International Federation of Clinical Neurophysiology.
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    Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables
    (Elsevier B.V., 2013-03-10) Martins, David C.; Oliveira, Evaldo A. de [UNIFESP]; Braga-Neto, Ulisses M.; Hashimoto, Ronaldo F.; Cesar, Roberto M.; Fed Univ ABC; Universidade Federal de São Paulo (UNIFESP); Universidade de São Paulo (USP); Brazilian Bioethanol Sci & Technol Lab; Texas A&M Univ
    A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. in a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. in addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.