Network Model Selection for Task-Focused Attributed Network Inference

Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments

Learning Deep Latent Spaces for Multi-Label Classification

Multi-label classification is a practical yet challenging task in machine learning related fields, since it requires the prediction of more than one label category for each input instance. We propose a novel deep neural networks (DNN) based model, Canonical Correlated AutoEncoder (C2AE), for solving this task. Aiming at better relating feature and label domain data for improved classification, we uniquely perform joint feature and label embedding by deriving a deep latent space, followed by the introduction of label-correlation sensitive loss function for recovering the predicted label outputs. Our C2AE is achieved by integrating the DNN architectures of canonical correlation analysis and autoencoder, which allows end-to-end learning and prediction with the ability to exploit label dependency. Moreover, our C2AE can be easily extended to address the learning problem with missing labels. Our experiments on multiple datasets with different scales confirm the effectiveness and robustness of our proposed method, which is shown to perform favorably against state-of-the-art methods for multi-label classification.Comment: published in AAAI-201

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

Label Mask for Multi-Label Text Classification

One of the key problems in multi-label text classification is how to take advantage of the correlation among labels. However, it is very challenging to directly model the correlations among labels in a complex and unknown label space. In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model. LM-MTC is able to capture implicit relationships among labels through the powerful ability of pre-train language models. On the basis, we assign a different token to each potential label, and randomly mask the token with a certain probability to build a label based Masked Language Model (MLM). We train the MTC and MLM together, further improving the generalization ability of the model. A large number of experiments on multiple datasets demonstrate the effectiveness of our method