Monte Carlo feature selection for supervised classification

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...

Monte Carlo Feature Selection and Interdependency Discovery in Supervised Classification

Applications of machine learning techniques in Life Sciences are the main applications forcing a paradigm shift in the way these techniques are used. Rather than obtaining the best possible supervised classifier, the Life Scientist needs to know which features contribute best to classifying observations into distinct classes and what are the interdependencies between the features. To this end w...

Selection of Significant Features Using Monte Carlo Feature Selection

Feature selection methods identify subsets of features in large datasets. Such methods have become popular in data-intensive areas, and performing feature selection prior tomodel constructionmay reduce the computational cost and improve the model quality. Monte Carlo Feature Selection (MCFS) is a feature selection method aimed at finding features to use for classification. Here we suggest a str...

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