A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks

info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT" Custode, L. L., Tecce, C. L., Bakurov, I., Castelli, M., Cioppa, A. D., & Vanneschi, L. (2020). A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks. In P. A. Castillo, J. L. Jiménez Laredo, & F. Fernández de Vega (Eds.), Applications of Evolutionary Computation - 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings (pp. 513-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12104 LNCS). Springer. https://doi.org/10.1007/978-3-030-43722-0_33In recent years neuroevolution has become a dynamic and rapidly growing research field. Interest in this discipline is motivated by the need to create ad-hoc networks, the topology and parameters of which are optimized, according to the particular problem at hand. Although neuroevolution-based techniques can contribute fundamentally to improving the performance of artificial neural networks (ANNs), they present a drawback, related to the massive amount of computational resources needed. This paper proposes a novel population-based framework, aimed at finding the optimal set of synaptic weights for ANNs. The proposed method partitions the weights of a given network and, using an optimization heuristic, trains one layer at each step while “freezing” the remaining weights. In the experimental study, particle swarm optimization (PSO) was used as the underlying optimizer within the framework and its performance was compared against the standard training (i.e., training that considers the whole set of weights) of the network with PSO and the backward propagation of the errors (backpropagation). Results show that the subsequent training of sub-spaces reduces training time, achieves better generalizability, and leads to the exhibition of smaller variance in the architectural aspects of the network.authorsversionpublishe

"Choice set" for health behavior in choice-constrained settings to frame research and inform policy : examples of food consumption, obesity and food security

ABSTRACT: Objectives: Using the nexus between food consumption, food security and obesity, this paper addresses the complexity of health behavior decision-making moments that reflect relational social dynamics in context-specific dialogues, often in choice-constrained conditions. Methods: A pragmatic review of literature regarding social determinants of health in relation to food consumption, food security and obesity was used to advance this theoretical model. Results and discussion: We suggest that health choice, such as food consumption, is based on more than the capacity and volition of individuals to make "healthy" choices, but is dialogic and adaptive. In terms of food consumption, there will always be choice-constrained conditions, along a continuum representing factors over which the individual has little or no control, to those for which they have greater agency. These range from food store geographies and inventories and food availability, logistical considerations such as transportation, food distribution, the structure of equity in food systems, state and non-government food and nutrition programs, to factors where the individual exercises a greater degree of autonomy, such as sociocultural foodways, family and neighborhood shopping strategies, and personal and family food preferences. At any given food decision-making moment, many factors of the continuum are present consciously or unconsciously when the individual makes a decision. These health behavior decision-making moments are mutable, whether from an individual perspective, or within a broader social or policy context. We review the construct of "choice set", the confluence of factors that are temporally weighted by the differentiated and relationally-contextualized importance of certain factors over others in that moment. The choice transition represents an essential shift of the choice set based on the conscious and unconscious weighting of accumulated evidence, such that people can project certain outcomes. Policies and interventions should avoid dichotomies of "good and bad" food choices or health behaviors, but focus on those issues that contribute to the weightedness of factors influencing food choice behavior at a given decision-making moment and within a given choice set

Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives

Gonçalves, I., Seca, M., & Castelli, M. (2020). Explorations of the Semantic Learning Machine Neuroevolution Algorithm: Dynamic Training Data Use, Ensemble Construction Methods, and Deep Learning Perspectives. In W. Banzhaf, E. Goodman, L. Sheneman, L. Trujillo, & B. Worzel (Eds.), Genetic Programming Theory and Practice XVII: Genetic and Evolutionary Computation (pp. 39-62). [Chapter 3] (Genetic Programming Theory and Practice XVII). Springer. https://doi.org/10.1007/978-3-030-39958-0_3The recently proposed Semantic Learning Machine (SLM) neuroevolution algorithm is able to construct Neural Networks (NNs) over unimodal error landscapes in any supervised learning problem where the error is measured as a distance to the known targets. This chapter studies how different methods of dynamically using the training data affect the resulting generalization of the SLM algorithm. Across four real-world binary classification datasets, SLM is shown to outperform the Multi-layer Perceptron, with statistical significance, after parameter tuning is performed in both algorithms. Furthermore, this chapter also studies how different ensemble constructions methods influence the resulting generalization. The results show that the stochastic nature of SLM already confers enough diversity to the ensembles such that Bagging and Boosting cannot improve upon a simple averaging ensemble construction method. Finally, some initial results with SLM and Convolutional NNs are presented and future Deep Learning perspectives are discussed.authorsversionpublishe

CRIT-LINE: a noninvasive tool to monitor hemoglobin levels in pediatric hemodialysis patients

The national average for achieving the KDOQI-recommended hemoglobin (Hgb) target level of 11-12 g/dL is low with the current anemia management protocol of measuring Hgb levels every 2-4 weeks to guide intervention. The objective of this study was to correlate initial Hgb readings from the CRIT-LINE monitor with actual serum Hgb levels in pediatric patients on hemodialysis (HD).Data were collected from pediatric HD patients who had Hgb tests ordered for routine and/or clinical reasons. Hgb concentrations were read with the CRIT-LINE after 0.5 or 1 L of blood had been processed by HD in patients with a body weight of ≤20 or >20 kg, respectively. Ultrafiltration was kept at a minimum until the CRIT-LINE Hgb was read.In total, 217 Hgb readings from 23 HD patients were analyzed. Results showed a statistically significant correlation between CRIT-LINE readings and laboratory Hgb measurements (r = 0.94, p < 0.0001) using Pearson correlation coefficients for well-distributed data. The mean Hgb levels measured by CRIT-LINE and the laboratory were 11.12 ± 1.63 and 11.31 ± 1.69 g/dL, respectively.The CRIT-LINE monitor is an accurate instrument for monitoring Hgb levels in HD patients. Further studies will be needed to evaluate whether using CRIT-LINE Hgb levels to guide anemia management will improve the percentage of children with Hgb levels within target