Improving classification using a Confidence Matrix based on weak classifiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Confidence Matrix (CM). This representation consists of posterior probability values provided by several weak classifiers, each one trained and used in different sets of features from the original sample. The CM allows the final classifier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of CM permits a significant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classifiers with the k-Nearest Neighbor rule as a weak classifier and by applying statistical significance tests.

Associative Behaviours and Political Participation of EU migrants

Civic culture is structured on a network of interpersonal associations with different degrees of formalization. According to theories on civic and political action, certain agents, such as associations, play a key role in setting targets, socializing or coordinating sociopolitical actions, among other functions. Associations strengthen the political and civic system of societies. Likewise, they are a vehicle for individuals’ integration, which is particularly important in the case of immigrants. For these, associations are both a vehicle for integration and an instrument for political participation. This article explores the use and purpose of associations according to immigrants from Romania, Poland, the United Kingdom and Germany living in Spain.

SARASOM: a supervised architecture based on the recurrent associative SOM

We present and evaluate a novel supervised recurrent neural network architecture, the SARASOM, based on the associative self-organizing map. The performance of the SARASOM is evaluated and compared with the Elman network as well as with a hidden Markov model (HMM) in a number of prediction tasks using sequences of letters, including some experiments with a reduced lexicon of 15 words. The results were very encouraging with the SARASOM learning better and performing with better accuracy than both the Elman network and the HMM.

Contribution of individual to collective brands

Traditionally, literature estimates the equity of a brand or its extension but it pays little attention to collective brand equity even though collective branding is increasingly used to differentiate the homogenous products of different firms or organizations. We propose an approach that estimates the incremental effect of individual brands (or the contribution of individual brands) on collective brand equity through the various stages of a consumer hierarchical buying choice process in which decisions are nested: “whether to buy”, “what collective brand to buy” and “what individual brand to buy”. This proposal follows the approach of the Random Utility Theory, and it is theoretically argued through the Associative Networks Theory and the cybernetic model of decision making. The empirical analysis carried out in the area of collective brands in Spanish tourism finds a three-stage hierarchical sequence, and estimates the contribution of individual brands to the equity of the collective brands of “Sun, Sea and Sand” and of “World Heritage Cities”.

Nueva técnica de fusión de clasificadores aplicada a la mejora de la segmentación de audio

Este artículo presenta un nuevo algoritmo de fusión de clasificadores a partir de su matriz de confusión de la que se extraen los valores de precisión (precision) y cobertura (recall) de cada uno de ellos. Los únicos datos requeridos para poder aplicar este nuevo método de fusión son las clases o etiquetas asignadas por cada uno de los sistemas y las clases de referencia en la parte de desarrollo de la base de datos. Se describe el algoritmo propuesto y se recogen los resultados obtenidos en la combinación de las salidas de dos sistemas participantes en la campaña de evaluación de segmentación de audio Albayzin 2012. Se ha comprobado la robustez del algoritmo, obteniendo una reducción relativa del error de segmentación del 6.28% utilizando para realizar la fusión el sistema con menor y mayor tasa de error de los presentados a la evaluación.

Aplicaciones de procesamiento de lenguaje natural

El campo de procesamiento de lenguaje natural (PLN), ha tenido un gran crecimiento en los últimos años; sus áreas de investigación incluyen: recuperación y extracción de información, minería de datos, traducción automática, sistemas de búsquedas de respuestas, generación de resúmenes automáticos, análisis de sentimientos, entre otras. En este artículo se presentan conceptos y algunas herramientas con el fin de contribuir al entendimiento del procesamiento de texto con técnicas de PLN, con el propósito de extraer información relevante que pueda ser usada en un gran rango de aplicaciones. Se pueden desarrollar clasificadores automáticos que permitan categorizar documentos y recomendar etiquetas; estos clasificadores deben ser independientes de la plataforma, fácilmente personalizables para poder ser integrados en diferentes proyectos y que sean capaces de aprender a partir de ejemplos. En el presente artículo se introducen estos algoritmos de clasificación, se analizan algunas herramientas de código abierto disponibles actualmente para llevar a cabo estas tareas y se comparan diversas implementaciones utilizando la métrica F en la evaluación de los clasificadores.

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%.

Promoter effect of sodium in graphene-supported Ni and Ni–CeO2 catalyst for the low-temperature WGS reaction

The low temperature water–gas shift (WGS) reaction has been studied over Ni–CeO2/Graphene and Ni/Graphene. The catalysts were prepared with 5 wt.% Ni and 20 wt.% CeO2 loadings, by deposition-precipitation employing sodium hydroxide and urea as precipitating agents. The materials were characterized by TEM, powder X-ray diffraction, Raman spectroscopy, H2-temperature-programmed reduction and X-ray photoelectron spectroscopy (XPS). The characterization and the reaction results indicated that the interaction between the active species and the support is higher than with activated carbon, and this hinders the reducibility of ceria and thus the catalytic performance. On the other hand, the presence of residual sodium in samples prepared by precipitation with NaOH facilitated the reduction of ceria. The catalytic activity was highly improved in the presence of sodium, what can be explained on the basis of an associative reaction mechanism which is favored over Ni-O-Na entities.

Human Behaviour Recognition based on Trajectory Analysis using Neural Networks

Automated human behaviour analysis has been, and still remains, a challenging problem. It has been dealt from different points of views: from primitive actions to human interaction recognition. This paper is focused on trajectory analysis which allows a simple high level understanding of complex human behaviour. It is proposed a novel representation method of trajectory data, called Activity Description Vector (ADV) based on the number of occurrences of a person is in a specific point of the scenario and the local movements that perform in it. The ADV is calculated for each cell of the scenario in which it is spatially sampled obtaining a cue for different clustering methods. The ADV representation has been tested as the input of several classic classifiers and compared to other approaches using CAVIAR dataset sequences obtaining great accuracy in the recognition of the behaviour of people in a Shopping Centre.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.

Modelo de negocio para mejorar la competitividad de la cadena productiva del cuy en Lambayeque-Perú

El principal problema que atraviesan los pequeños productores en la región Lambayeque y en su mayoría a nivel nacional es el bajo nivel de competitividad en sus respectivas cadenas productivas, esto se debe a que no cuentan con una adecuada gestión asociativa y empresarial, desconocen técnicas en manejos productivo y presentan deficiente articulación comercial teniendo como efecto directo bajos niveles de calidad de vida. La presente investigación tiene como objetivo principal plantear un modelo de negocio para mejorar la competitividad de la cadena productiva del cuy en una organización cooperativa de productores de cuyes del distrito de Mórrope, identificando los factores que determinan la competitividad, además plasmando los diversos elementos, componentes, estrategias y actividades que se unen para concretar el objetivo principal. Se presentará un diagnóstico inicial de la Cooperativa en estudio, los datos obtenidos son resultado de visitas y talleres con los asociados quienes respondieron a una encuesta y entrevistas, así mismo para el diagnóstico regional de la cadena del cuy se realizó la búsqueda de información primaria y secundaria, reforzado con entrevistas a especialistas del tema a nivel regional. Finalmente se utilizó la herramienta del marco lógico y del lienzo de modelo de negocio “Canvas” para plasmar la propuesta de mejora de la competitividad en beneficio de la cooperativa en estudio y que servirá como base para replicarse en otras organizaciones siendo acompañada de propuestas reales, validadas por especialistas.