Content-Based Quality Estimation for Automatic Subject Indexing of Short Texts under Precision and Recall Constraints

Semantic annotations have to satisfy quality constraints to be useful for digital libraries, which is particularly challenging on large and diverse datasets. Confidence scores of multi-label classification methods typically refer only to the relevance of particular subjects, disregarding indicators of insufficient content representation at the document-level. Therefore, we propose a novel approach that detects documents rather than concepts where quality criteria are met. Our approach uses a deep, multi-layered regression architecture, which comprises a variety of content-based indicators. We evaluated multiple configurations using text collections from law and economics, where the available content is restricted to very short texts. Notably, we demonstrate that the proposed quality estimation technique can determine subsets of the previously unseen data where considerable gains in document-level recall can be achieved, while upholding precision at the same time. Hence, the approach effectively performs a filtering that ensures high data quality standards in operative information retrieval systems.Comment: authors' manuscript, paper submitted to TPDL-2018 conference, 12 page

Random forests with random projections of the output space for high dimensional multi-label classification

We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage

Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain

In this paper we apply multilabel classification algorithms to the EUR-Lex database of legal documents of the European Union. For this document collection, we studied three different multilabel classification problems, the largest being the categorization into the EUROVOC concept hierarchy with almost 4000 classes. We evaluated three algorithms: (i) the binary relevance approach which independently trains one classifier per label; (ii) the multiclass multilabel perceptron algorithm, which respects dependencies between the base classifiers; and (iii) the multilabel pairwise perceptron algorithm, which trains one classifier for each pair of labels. All algorithms use the simple but very efficient perceptron algorithm as the underlying classifier, which makes them very suitable for large-scale multilabel classification problems. The main challenge we had to face was that the almost 8,000,000 perceptrons that had to be trained in the pairwise setting could no longer be stored in memory. We solve this problem by resorting to the dual representation of the perceptron, which makes the pairwise approach feasible for problems of this size. The results on the EUR-Lex database confirm the good predictive performance of the pairwise approach and demonstrates the feasibility of this approach for large-scale tasks

Efficient multilabel classification algorithms for large-scale problems in the legal domain

In this paper we evaluate the performance of multilabel classification algorithms on the EUR-Lex database of legal documents of the European Union. On the same set of underlying documents, we defined three different large-scale multilabel problems with up to 4000 classes. On these datasets, we compared three algorithms: (i) the well-known one-against-all approach (OAA); (ii) the multiclass multilabel perceptron algorithm (MMP), which modifies the OAA ensemble by respecting dependencies between the base classifiers in the training protocol of the classifier ensemble; and (iii) the multilabel pairwise perceptron algorithm (MLPP), which unlike the previous algorithms trains one base classifier for each pair of classes. All algorithms use the simple but very efficient perceptron algorithm as the underlying classifier. This makes them very suitable for large-scale multilabel classification problems. While previous work has already shown that the latter approach outperforms the other two approaches in terms of predictive accuracy, its key problem is that it has to store one classifier for each pair of classes. The key contribution of this work is to demonstrate a novel technique that makes the pairwise approach feasible for problems with large number of classes, such as those studied in this work. Our results on the EUR-Lex database illustrate the effectiveness of the pairwise approach and the efficiency of the MMP algorithm. We also show that it is feasible to efficiently and effectively handle very large multilabel problems

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