Feature selection has become an indispensable part of intelligent systems, especially with the proliferation of high dimensional data. It identifies the subset of discriminative features leading to better learning performances, i.e., higher learning accuracy, lower computational cost and significant model interpretability. This paper proposes a new efficient unsupervised feature selection method based on graph centrality and subspace learning called UGFS for 'Unsupervised Graph-based Feature Selection'. The method maps features on an affinity graph where the relationships (edges) between feature nodes are defined by means of data points subspace preference. Feature importance score is then computed on the entire graph using a centrality measure. For this purpose, we investigated the Google's PageRank method originally introduced to rank web-pages. The proposed feature selection method has been evaluated using classification and redundancy rates measured on the selected feature subsets. Comparisons with the well-known unsupervised feature selection methods, on gene/expression benchmark datasets, demonstrate the validity and the efficiency of the proposed method.

Monitoring patients at risk of epileptic seizure is critical for optimal treatment and ensuing the reduction of seizure risk and complications. In general, seizure detection is done manually in hospitals and involves time-consuming visual inspection and interpretation by experts of electroencephalography (EEG) recordings. The purpose of this study is to investigate the pertinence of band-limited spectral power and signal complexity in order to discriminate between seizure and seizure-free EEG brain activity. The signal complexity and spectral power are evaluated in five frequency intervals, namely, the delta, theta, alpha, beta, and gamma bands, to be used as EEG signal feature representation. Classification of seizure and seizure-free data was performed by prevalent potent classifiers. Substantial comparative performance evaluation experiments were performed on a large EEG data record of 341 patients in the Temple University Hospital EEG seizure database. Based on statistically validated criteria, results show the efficiency of band-limited spectral power and signal complexity when using random forest and gradient-boosting decision tree classifiers (95% of the area under the curve (AUC) and 91% for both F-measure and accuracy). These results support the use of these automatic classification schemes to assist the practicing neurologist interpret EEG records more accurately and without tedious visual inspection.

Nowadays, about 1.25 million people die each year as a result of road traffic crashes (World Health Organization WHO). Without sustained action, road traffic crashes are predicted to become the leading cause of death by 2030, including among the growing population of senior drivers. 10-20% of all traffic accidents are made by nonvigilant drivers and 60% of these accidents are due to fatigue (Awake Consortium 2001). Fatigue is defined as a transitional state between awakenings and sleeping. It affects the skills required for a safe driving, by increasing the driving error frequency as well as their amplitudes and variability. It also reduces the driver perception and decision-making capability to control the vehicle (Sahayadhas et al. 2012). Fatigue depends on several endogenous and exogenous factors, for example, age, motivation and driving time. Otmani et al. (2005) demonstrated that drivers are fatigued after 20 min of driving, and countermeasures must be considered before the driver reaches

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