BUSCADOR RECOLECTA

In this work we propose a new algorithm to train and optimize an ensemble of classifiers. We call this algorithm the Krypteia ensemble, based on an ancient Spartan tradition designed to convert their most promising individuals into future leaders of their society. We show how to adapt this ancient custom to optimize classifiers by generating different variations of the same task, each one offering different hardships according to distinct stochastic variables. This is thus applied to induce diversity in the set of individual weak learners. Then, we use a set of agents designed to select those subjects who excel in their assignments, and whose interaction minimizes excessive redundancies in the resulting population. We also study how different Krypteia ensembles can be stacked together, so that more complex classifiers can be built using the same procedure. Besides, we consider a wide range of different aggregation functions in the decision making phase to find the optimal performance for the different Krypteia ensemble variations tested. Finally, we study how different Krypteia ensembles perform for a wide range of classification datasets and we compare them with other state-of-the-art design techniques of classifier ensembles, obtaining favourable results to our proposal., Javier Fumanal Idocin and Humberto Bustince's research has been supported by project PID2019-108392 GB I00 (AEI/10.13039/ 501100011033). Oscar Cordón's research has been funded by the Spanish Ministry of Science and Innovation (MICIN), Agencia Estatal de Investigación (AEI), Spain, under grant CONFIA (PID2021-122916NB-I00), and by the Regional Government of Andalusia under grant EXAISFI (P18-FR-4262), both including European Regional Development Funds (ERDF). Open access funding provided by Universidad Pública de Navarra.

Automatic art analysis employs different image processing techniques to classify and categorize works of art. When working with artistic images, we need to take into account further considerations compared to classical image processing. This is because artistic paintings change drastically depending on the author, the scene depicted, and their artistic style. This can result in features that perform very well in a given task but do not grasp the whole of the visual and symbolic information contained in a painting. In this article, we show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We present different methods to improve the generalization capabilities and performance of artistic classification systems. Furthermore, we propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model considering fuzzy rules. These rules show the patterns and variables that are relevant to solve each task and how effective is each of the patterns found. Our results show that compared to multitask learning, our proposed context-aware features can achieve up to 19% more accurate results when using the residual network architecture and 3% when using ConvNeXt. We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than other kinds of features., This work was supported in part by the Oracle Cloud credits and related resources provided by Oracle, in part by the MCIN/AEI/10.13039/501100011033 and ERDF "A way of making Europe" under Grant CONFIA PID2021-122916NB-I00, and in part by the MCIN Project PID2022-136627NB-I00.

Social network analysis is a popular tool to understand the relationships between interacting agents by studying the structural properties of their connections. However, this kind of analysis can miss some of the domain-specific knowledge available in the original information domain and its propagation through the associated network. In this work, we develop an extension of classical social network analysis to incorporate external information from the original source of the network. With this extension we propose a new centrality measure, the semantic value, and a new affinity function, the semantic affinity, that establishes fuzzy-like relationships between the different actors in the network. We also propose a new heuristic algorithm based on the shortest capacity problem to compute this new function. As an illustrative case study, we use the novel proposals to analyze and compare the gods and heroes from three different classical mythologies: 1) Greek; 2) Celtic; and 3) Nordic. We study the relationships of each individual mythology and those of the common structure that is formed when we fuse the three of them. We also compare our results with those obtained using other existing centrality measures and embedding approaches. In addition, we test the proposed measures on a classical social network, the Reuters terror news network, as well as in a Twitter network related to the COVID-19 pandemic. We found that the novel method obtains more meaningful comparisons and results than previous existing approaches in every case., The work of Javier Fumanal-Idocin and Humberto
Bustince was supported by the Project PID2019-108392GB I00 under Grant
AEI/10.13039/501100011033. The work of Oscar Cordón was supported by
the Government of Spain under Grant MCIN/AEI/10.13039/501100011033/
and CONFIA under Grant PID2021-122916NB-I00, including European
Regional Development Funds (ERDF) “FEDER, Una manera de hacer
Europa.” The work of Antonio-Francisco Roldán López-de-Hierro was
supported in part by the Project PID2020-119478GB-I00 and in part
by the FEDER Project under Grant A-FQM-170-UGR20