Alejandro Rosete

TL;DR: This paper proposes MOPNAR, a new multiobjective evolutionary algorithm, in order to mine a reduced set of positive and negative quantitative association rules with low computational cost and maximizes three objectives-comprehensibility, interestingness, and performance-in order to obtain rules that are interesting, easy to understand, and provide good coverage of the dataset.

TL;DR: NICGAR is presented, a new Niching Genetic Algorithm to obtain a reduce set of different positive and negative quantitative association rules with a low runtime to extract the rules based on the existence of a pool of external solutions that contains the best rule of each niche found in the search process.

TL;DR: This paper proposes a new multi-objective evolutionary model which maximizes the comprehensibility, interestingness and performance of the objectives in order to mine a set of quantitative association rules with a good trade-off between interpretability and accuracy.

TL;DR: A dataset of 64 signs from the Argentinian Sign Language, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks.

TL;DR: The study shows that the best evolution strategy improves upon the accuracy obtained by the standard greedy method (BPA) by 35%, and that of LIA G M P 2 by 12.5%.