Modelos de clasificación multi-etiqueta para datos heterogéneos: un enfoque basado en ensembles

In recent years, the multi-label classification task has gained the attention of the scientific community given its ability to solve real-world problems where each instance of the dataset may be associated with several class labels simultaneously. For example, in medical problems each patient may be affected by several diseases at the same time, and in multimedia categorization problems, each item might be related with different tags or topics. Thus, given the nature of these problems, dealing with them as traditional classification problems where just one class label is assigned to each instance, would lead to a lose of information. However, the fact of having more than one label associated with each instance leads to new classification challenges that should be addressed, such as modeling the compound dependencias among labels, the imbalance of the label space, and the high dimensionality of the output space. A large number of methods for multi-label classification has been proposed in the literature, including several ensemble-based methods. Ensemble learning is a technique which is based on combining the outputs of many diverse base models, in order to outperform each of the separate members. In multi-label classification, ensemble methods are those that combine the predictions of several multi-label classifiers, and these methods have shown to outperform simpler multi-label classifiers. Therefore, given its great performance, we focused our research on the study of ensemble-based methods for multi-label classification. The first objective of this dissertation is to perform an thorough review of the state-of-the-art ensembles of multi-label classifiers. Its aim is twofold: I) study different ensembles of multi-label classifiers proposed in the literature, and categorize them according to their characteristics proposing a novel taxonomy; and II) perform an experimental study to find the method or family of methods that performs better depending on the characteristics of the data, as well as provide then some guidelines to select the best method according to the characteristics of a given problem. Since most of the ensemble methods for multi-label classification are based on creating diverse members by randomly selecting instances, input features, or labels, our second and main objective is to propose novel ensemble methods for multi-label classification where the characteristics of the data are taken into account. For this purpose, we first propose an evolutionary algorithm able to build an ensemble of multi-label classifiers, where each of the individuals of the population is an entire ensemble. This approach is able to model the relationships among the labels with a relative low complexity and imbalance of the output space, also considering these characteristics to guide the learning process. Furthermore, it looks for an optimal structure of the ensemble not only considering its predictive performance, but also the number of times that each label appears in it. In this way, all labels are expected to appear a similar number of times in the ensemble, not neglecting any of them regardless of their frequency. Then, we develop a second evolutionary algorithm able to build ensembles of multi-label classifiers, but in this case each individual of the population is a hypothetical member of the ensemble, and not the entire ensemble. The fact of evolving members of the ensemble separately makes the algorithm less computationally complex and able to determine the quality of each member separately. However, a method to select the ensemble members needs to be defined. This process selects those classifiers that are both accurate but also diverse among them to form the ensemble, also controlling that all labels appear a similar number of times in the final ensemble. In all experimental studies, the methods are compared using rigorous experimental setups and statistical tests over many evaluation metrics and reference datasets in multi-label classification. The experiments confirm that the proposed methods obtain significantly better and more consistent performance than the stateof- the-art methods in multi-label classification. Furthermore, the second proposal is proven to be more efficient than the first one, given the use of separate classifiers as individuals.En los últimos años, el paradigma de clasificación multi-etiqueta ha ganado atención en la comunidad científica, dada su habilidad para resolver problemas reales donde cada instancia del conjunto de datos puede estar asociada con varias etiquetas de clase simultáneamente. Por ejemplo, en problemas médicos cada paciente puede estar afectado por varias enfermedades a la vez, o en problemas de categorización multimedia, cada ítem podría estar relacionado con varias etiquetas o temas. Dada la naturaleza de estos problemas, tratarlos como problemas de clasificación tradicional donde cada instancia puede tener asociada únicamente una etiqueta de clase, conllevaría una pérdida de información. Sin embargo, el hecho de tener más de una etiqueta asociada con cada instancia conlleva la aparición de nuevos retos que deben ser abordados, como modelar las dependencias entre etiquetas, el desbalanceo de etiquetas, y la alta dimensionalidad del espacio de salida. En la literatura se han propuesto un gran número de métodos para clasificación multi-etiqueta, incluyendo varios basados en ensembles. El aprendizaje basado en ensembles combina las salidas de varios modelos más simples y diversos entre sí, de cara a conseguir un mejor rendimiento que cada miembro por separado. En clasificación multi-etiqueta, se consideran ensembles aquellos métodos que combinan las predicciones de varios clasificadores multi-etiqueta, y estos métodos han mostrado conseguir un mejor rendimiento que los clasificadores multi-etiqueta sencillos. Por tanto, dado su buen rendimiento, centramos nuestra investigación en el estudio de métodos basados en ensembles para clasificación multi-etiqueta. El primer objetivo de esta tesis el realizar una revisión a fondo del estado del arte en ensembles de clasificadores multi-etiqueta. El objetivo de este estudio es doble: I) estudiar diferentes ensembles de clasificadores multi-etiqueta propuestos en la literatura, y categorizarlos de acuerdo a sus características proponiendo una nueva taxonomía; y II) realizar un estudio experimental para encontrar el método o familia de métodos que obtiene mejores resultados dependiendo de las características de los datos, así como ofrecer posteriormente algunas guías para seleccionar el mejor método de acuerdo a las características de un problema dado. Dado que la mayoría de ensembles para clasificación multi-etiqueta están basados en la creación de miembros diversos seleccionando aleatoriamente instancias, atributos, o etiquetas; nuestro segundo y principal objetivo es proponer nuevos modelos de ensemble para clasificación multi-etiqueta donde se tengan en cuenta las características de los datos. Para ello, primero proponemos un algoritmo evolutivo capaz de generar un ensemble de clasificadores multi-etiqueta, donde cada uno de los individuos de la población es un ensemble completo. Este enfoque es capaz de modelar las relaciones entre etiquetas con una complejidad y desbalanceo de etiquetas relativamente bajos, considerando también estas características para guiar el proceso de aprendizaje. Además, busca una estructura óptima para el ensemble, no solo considerando su capacidad predictiva, pero también teniendo en cuenta el número de veces que aparece cada etiqueta en él. De este modo, se espera que todas las etiquetas aparezcan un número de veces similar en el ensemble, sin despreciar ninguna de ellas independientemente de su frecuencia. Posteriormente, desarrollamos un segundo algoritmo evolutivo capaz de construir ensembles de clasificadores multi-etiqueta, pero donde cada individuo de la población es un hipotético miembro del ensemble, en lugar del ensemble completo. El hecho de evolucionar los miembros del ensemble por separado hace que el algoritmo sea menos complejo y capaz de determinar la calidad de cada miembro por separado. Sin embargo, también es necesario definir un método para seleccionar los miembros que formarán el ensemble. Este proceso selecciona aquellos clasificadores que sean tanto precisos como diversos entre ellos, también controlando que todas las etiquetas aparezcan un número similar de veces en el ensemble final. En todos los estudios experimentales realizados, los métodos han sido comparados utilizando rigurosas configuraciones experimentales y test estadísticos, involucrando varias métricas de evaluación y conjuntos de datos de referencia en clasificación multi-etiqueta. Los experimentos confirman que los métodos propuestos obtienen un rendimiento significativamente mejor y más consistente que los métodos en el estado del arte. Además, se demuestra que el segundo algoritmo propuesto es más eficiente que el primero, dado el uso de individuos representando clasificadores por separado

Multi-label classification models for heterogeneous data: an ensemble-based approach.

In recent years, the multi-label classification gained attention of the scientific community given its ability to solve real-world problems where each instance of the dataset may be associated with several class labels simultaneously, such as multimedia categorization or medical problems. The first objective of this dissertation is to perform a thorough review of the state-of-the-art ensembles of multi-label classifiers (EMLCs). Its aim is twofold: 1) study state-of-the-art ensembles of multi-label classifiers and categorize them proposing a novel taxonomy; and 2) perform an experimental study to give some tips and guidelines to select the method that perform the best according to the characteristics of a given problem. Since most of the EMLCs are based on creating diverse members by randomly selecting instances, input features, or labels, our main objective is to propose novel ensemble methods while considering the characteristics of the data. In this thesis, we propose two evolutionary algorithms to build EMLCs. The first proposal encodes an entire EMLC in each individual, where each member is focused on a small subset of the labels. On the other hand, the second algorithm encodes separate members in each individual, then combining the individuals of the population to build the ensemble. Finally, both methods are demonstrated to be more consistent and perform significantly better than state-of-the-art methods in multi-label classification

Semantics and statistics for automated image annotation

Automated image annotation consists of a number of techniques that aim to find the correlation between words and image features such as colour, shape, and texture to provide correct annotation words to images. In particular, approaches based on Bayesian theory use machine-learning techniques to learn statistical models from a training set of pre-annotated images and apply them to generate annotations for unseen images. The focus of this thesis lies in demonstrating that an approach, which goes beyond learning the statistical correlation between words and visual features and also exploits information about the actual semantics of the words used in the annotation process, is able to improve the performance of probabilistic annotation systems. Specifically, I present three experiments. Firstly, I introduce a novel approach that automatically refines the annotation words generated by a non-parametric density estimation model using semantic relatedness measures. Initially, I consider semantic measures based on co-occurrence of words in the training set. However, this approach can exhibit limitations, as its performance depends on the quality and coverage provided by the training data. For this reason, I devise an alternative solution that combines semantic measures based on knowledge sources, such as WordNet and Wikipedia, with word co-occurrence in the training set and on the web, to achieve statistically significant results over the baseline. Secondly, I investigate the effect of using semantic measures inside an evaluation measure that computes the performance of an automated image annotation system, whose annotation words adopt the hierarchical structure of an ontology. This is the case of the ImageCLEF2009 collection. Finally, I propose a Markov Random Field that exploits the semantic context dependencies of the image. The best result obtains a mean average precision of 0.32, which is consistent with the state-of-the-art in automated image annotation for the Corel 5k dataset. </br

Semantic multimedia modelling & interpretation for annotation

The emergence of multimedia enabled devices, particularly the incorporation of cameras in mobile phones, and the accelerated revolutions in the low cost storage devices, boosts the multimedia data production rate drastically. Witnessing such an iniquitousness of digital images and videos, the research community has been projecting the issue of its significant utilization and management. Stored in monumental multimedia corpora, digital data need to be retrieved and organized in an intelligent way, leaning on the rich semantics involved. The utilization of these image and video collections demands proficient image and video annotation and retrieval techniques. Recently, the multimedia research community is progressively veering its emphasis to the personalization of these media. The main impediment in the image and video analysis is the semantic gap, which is the discrepancy among a user’s high-level interpretation of an image and the video and the low level computational interpretation of it. Content-based image and video annotation systems are remarkably susceptible to the semantic gap due to their reliance on low-level visual features for delineating semantically rich image and video contents. However, the fact is that the visual similarity is not semantic similarity, so there is a demand to break through this dilemma through an alternative way. The semantic gap can be narrowed by counting high-level and user-generated information in the annotation. High-level descriptions of images and or videos are more proficient of capturing the semantic meaning of multimedia content, but it is not always applicable to collect this information. It is commonly agreed that the problem of high level semantic annotation of multimedia is still far from being answered. This dissertation puts forward approaches for intelligent multimedia semantic extraction for high level annotation. This dissertation intends to bridge the gap between the visual features and semantics. It proposes a framework for annotation enhancement and refinement for the object/concept annotated images and videos datasets. The entire theme is to first purify the datasets from noisy keyword and then expand the concepts lexically and commonsensical to fill the vocabulary and lexical gap to achieve high level semantics for the corpus. This dissertation also explored a novel approach for high level semantic (HLS) propagation through the images corpora. The HLS propagation takes the advantages of the semantic intensity (SI), which is the concept dominancy factor in the image and annotation based semantic similarity of the images. As we are aware of the fact that the image is the combination of various concepts and among the list of concepts some of them are more dominant then the other, while semantic similarity of the images are based on the SI and concept semantic similarity among the pair of images. Moreover, the HLS exploits the clustering techniques to group similar images, where a single effort of the human experts to assign high level semantic to a randomly selected image and propagate to other images through clustering. The investigation has been made on the LabelMe image and LabelMe video dataset. Experiments exhibit that the proposed approaches perform a noticeable improvement towards bridging the semantic gap and reveal that our proposed system outperforms the traditional systems

Nutrition, Health and Athletic Performance

Optimal nutrition is fundamental for enhancing training, recovery and performance in sport. Therefore, research has aimed to determine the efficacy of appropriate intake of nutrients, fluids, and supplements and their role in exercise performance. The purpose of this Special Issue entitled “Nutrition, Health and Athletic Performance” is to highlight recent research examining aspects of sports nutrition and exercise performance. Manuscript submissions of original research, meta-analyses, or reviews of the scientific literature, which targets nutritional strategies to benefit performance and health, are welcome. Studies performed in humans are preferred given the applied nature of this issue

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains

Proceedings of the Conference on Production Systems and Logistics: CPSL 2022

[no abstract available

Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Proceedings of the Seventh Italian Conference on Computational Linguistics CLiC-it 2020

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges