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A cost-sensitive constrained Lasso
The Lasso has become a benchmark data analysis procedure, and numerous variants have been proposed in the literature.
Variable selection for Naïve Bayes classification
However, features are usually correlated, a fact that violates the Na\"ive Bayes' assumption of conditional independence, and may deteriorate the method's performance.
Analysis of an aggregate loss model in a Markov renewal regime
Given a real operational risk database, the aggregate loss model is estimated by fitting separately the inter-losses times and severities.
Cost-sensitive Feature Selection for Support Vector Machines
The relevance of features in a classification procedure is linked to the fact that misclassifications costs are frequently asymmetric, since false positive and false negative cases may have very different consequences.
On support vector machines under a multiple-cost scenario
Such maximal margin hyperplane is obtained by solving a quadratic convex problem with linear constraints and integer variables.
Cost-sensitive probabilistic predictions for support vector machines
Classification in SVM is based on a score procedure, yielding a deterministic classification rule, which can be transformed into a probabilistic rule (as implemented in off-the-shelf SVM libraries), but is not probabilistic in nature.
