Feature Selection for Interpatient Supervised Heart Beat Classification

Feature Selection for Interpatient Supervised Heart Beat Classification

Supervised and interpatient classification of heart beats is primordial in many applications requiring long-term monitoring of the cardiac function. Several classification models able to cope with the strong class unbalance and a large variety of feature sets have been proposed for this task. In practice, over 200 features are often considered, and the features retained in the final model are e...

Monte Carlo feature selection for supervised classification

MOTIVATION Pre-selection of informative features for supervised classification is a crucial, albeit delicate, task. It is desirable that feature selection provides the features that contribute most to the classification task per se and which should therefore be used by any classifier later used to produce classification rules. In this article, a conceptually simple but computer-intensive approa...

BASSUM: A Bayesian semi-supervised method for classification feature selection

Feature selection is an important preprocessing step for building efficient, generalizable and interpretable classifiers on high dimensional data sets. Given the assumption on the sufficient labelled samples, the Markov Blanket provides a complete and sound solution to the selection of optimal features, by exploring the conditional independence relationships among the features. In real-world ap...

Feature Selection using Intensified Tabu Search for Supervised Classification

Feature Relevance Assessment in Automatic Inter-patient Heart Beat Classification

Long-term ECG recordings are often required for the monitoring of the cardiac function in clinical applications. Due to the high number of beats to evaluate, inter-patient computer-aided heart beat classification is of great importance for physicians. The main difficulty is the extraction of discriminative features from the heart beat time series. The objective of this work is the assessment of...