In this paper we propose a lazy learning strategy for building classification learning models. Instead of learning the models with the whole training data set before observing the new instance, a selection of patterns is made depending on the new query received and a classification model is learnt with those selected patterns. The selection of patterns is not homogeneous, in the sense that the number of selected patterns depends on the position of the query instance in the input space. That selection is made using a weighting function in order to give more importance to the training patterns that are more similar to the query instance. Our intention is to provide a lazy learning mechanism suited to any machine learning classification algorithm. For this reason, we study two different methods to avoid fixing any parameter. Experimental results show that classification rates of traditional machine learning algorithms based on trees, rules or functions can be improved when they are learnt with the lazy learning approach proposed.
Flow networks efficiently transport nutrients and other solutes in many physical systems, such as plant and animal vasculature. In the case of the animal circulatory system, an adequate oxygen and nutrient supply is not guaranteed everywhere: as nutrients travel through the microcirculation and get absorbed, they become less available at the venous side of the vascular network. Ensuring that the nutrient distribution is homogeneous provides a fitness advantage, as all tissue gets enough supply to survive while waste is minimized. How do animals build such a uniform perfusing flow system? We propose a local adaptation rule for the vessel radii that is able to equalize perfusion, while minimizing energy dissipation to circulate the flow and the material cost. The rule is a combination of different objective cost functions that compete to produce complex network morphologies ranging from hierarchical architectures to uniform mesh grids, depending on how each cost is weighted. We find that our local adaptation rules are consistent with experimental data of the rat mesentery vasculature.
In several countries, students learn basic mathematical skills first, followed by fractions before learning percentages. Previous longitudinal studies observed that basic mathematical skills (e.g., arithmetic) are significant predictors of understanding fractions, which is critical for later academic success. However, it is still unclear whether this predictive power generalizes to other types of rational numbers such as percentages. Here, we analyzed a large longitudinal dataset (2.798 students; 472.626 problem sets) from an online learning environment to predict performance in fraction understanding as well as processing percentages based on basic mathematical skills (i.e., arithmetic, measurement units, and geometry). As students learned fractions before percentages, we additionally assessed whether understanding fractions predicted processing percentages. We observed that performance in arithmetic, measurement units, and geometry significantly predicted performance on percentages and fraction understanding. We also found transfer effects from fractions to processing percentages. This indicates that processing percentages and understanding fractions similarly build on basic mathematical skills. Moreover, our results point towards the idea that knowledge attained on one format of rational numbers (fractions) may transfer to another format of rational numbers (percentages).
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