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Found 10 papers, 1 papers with code
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.
Generating collective counterfactual explanations in score-based classification via mathematical optimization
By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints.
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.
On Clustering Categories of Categorical Predictors in Generalized Linear Models
We propose a method to reduce the complexity of Generalized Linear Models in the presence of categorical predictors.
Optimal randomized classification trees
Our classifier can be seen as a randomized tree, since at each node of the decision tree a random decision is made.
Sparsity in Optimal Randomized Classification Trees
In this paper, we propose a continuous optimization approach to build sparse optimal classification trees, based on oblique cuts, with the aim of using fewer predictor variables in the cuts as well as along the whole tree.
