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Feature subset selection ( FSS) has received a great deal of attention in statistics, machine learning, and data mining. Real world data analyzed by data mining algorithms can involve a large number of redundant or irrelevant features or simply too many features for a learning algorithm to handle them efficiently. Feature selection is becoming essential as databases grow in size and complexity. The selection process is expected to bring benefits in terms of better performing models, computational efficiency, and simpler more understandable models. Evolutionary computation ( EC) encompasses a number of naturally inspired techniques such as genetic algorithms, genetic programming, ant colony optimization, or particle swarm optimization algorithms. Such techniques are well suited to feature selection because the representation of a feature subset is straightforward and the evaluation can also be easily accomplished through the use of wrapper or filter algorithms. Furthermore, the capability of such heuristic algorithms to efficiently search large search spaces is of great advantage to the feature selection problem. Here, we review the use of different EC paradigms for feature selection in classification problems. We discuss details of each implementation including representation, evaluation, and validation. The review enables us to uncover the best EC algorithms for FSS and to point at future research directions. WIREs Data Mining Knowl Discov 2013, 3:381-407. doi: 10.1002/widm.1106 Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. [ABSTRACT FROM AUTHOR]

Background. Orthostatic hypotension (OH) is common amongst the older population and is associated with morbidity and mortality. We sought to investigate predictors of OH to assist the clinician in identifying patients at risk. Methods and results. Database of 2696 patients attending a transient ischaemic attack (TIA) clinic between January 2006 and May 2009 was examined. Logistic regression models were constructed to determine clinical associates of OH. Demographics, co-morbidities, cardiovascular risk factors and medications were included in the multivariate models. Simple data mining models in the form of rule sets were developed for each component and they were assessed for predictive accuracy. The best models were validated on a smaller sample. Prevalence of OH was 22.3% in the TIA clinic population (50.6% men, mean 72 years; 49.4% women, mean 75 years). A significant postural drop in systolic blood pressure (BP) (≥ 20 mmHg) was more prevalent than a significant diastolic BP drop (≥ 10 mmHg). Isolated systolic hypertension was common (52.4%). Common factors predicting a significant systolic and diastolic BP fall were older age, previous TIA, being a current smoker, having diabetes and the use of beta-blockers. Both mean arterial and pulse pressure (MAP and PP) derived from supine BP were significantly associated with OH. Conclusions. OH should be assessed routinely in TIA clinics. MAP and PP may provide information on the predictability of OH. [ABSTRACT FROM AUTHOR]