Now showing items 1-6 of 6
Statistical model for reproducibility in ranking-based feature selection
The stability of feature subset selection algorithms has become crucial in real-world problems due to the need for consistent experimental results across different replicates. Specifically, in this paper, we analyze the ...
Supervised non-parametric discretization based on Kernel density estimation
Nowadays, machine learning algorithms can be found in many applications where the classifiers play a key role. In this context, discretizing continuous attributes is a common step previous to classification tasks, the main ...
Mallows and generalized Mallows model for matchings
The Mallows and Generalized Mallows Models are two of the most popular probability models for distribu- tions on permutations. In this paper, we consider both models under the Hamming distance. This models can be seen as ...
On the evaluation and selection of classifier learning algorithms with crowdsourced data
In many current problems, the actual class of the instances, the ground truth, is unavail- able. Instead, with the intention of learning a model, the labels can be crowdsourced by harvesting them from different annotators. ...
Bayesian inference for algorithm ranking analysis
The statistical assessment of the empirical comparison of algorithms is an essential step in heuristic optimization. Classically, researchers have relied on the use of statistical tests. However, recently, concerns about ...
Multi-objectivising Combinatorial Optimisation Problems by means of Elementary Landscape Decompositions
In the last decade, many works in combinatorial optimisation have shown that, due to the advances in multi-objective optimisation, the algorithms from this field could be used for solving single-objective problems as well. ...