Explicando el mundo en el que vivimos con mapas: propuesta de aprendizaje cartográfico

La cartografía se considera una herramienta útil para formar personas y educar a futuros ciudadanos. La enseñanza mediante mapas brinda la posibilidad a los estudiantes de que desarrollen habilidades de interpretación, comprensión y representación de su propio entorno, adquiriendo conocimiento geográfico espacial. El presente estudio se basa en analizar mediante encuestas, dirigidas al profesorado y alumnado de Educación Secundaria y Bachillerato, qué es lo que se hace en las aulas con los mapas, cuándo y cómo se usan, cuáles son las percepciones y conocimientos de los estudiantes. En consecuencia se pretende evitar algunos de los problemas detectados y ayudar a docentes y estudiantes en la enseñanza y aprendizaje de los conocimientos cartográficos mediante el uso de esta propuesta didáctica. Para ello se elabora una rúbrica como instrumento de evaluación con pautas en la elaboración de los mapas. Reafirmando así la hipótesis la cual la cartografía es una disciplina necesaria que impulsa competencias y capacidades cartográficas, siempre y cuando exista un aprendizaje guiado, progresivo, sistemático y adaptado.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Model Probability in Self-organising Maps

Growing models have been widely used for clustering or topology learning. Traditionally these models work on stationary environments, grow incrementally and adapt their nodes to a given distribution based on global parameters. In this paper, we present an enhanced unsupervised self-organising network for the modelling of visual objects. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product.