Measuring information integration

BACKGROUND: To understand the functioning of distributed networks such as the brain, it is important to characterize their ability to integrate information. The paper considers a measure based on effective information, a quantity capturing all causal interactions that can occur between two parts of a system. RESULTS: The capacity to integrate information, or Φ, is given by the minimum amount of effective information that can be exchanged between two complementary parts of a subset. It is shown that this measure can be used to identify the subsets of a system that can integrate information, or complexes. The analysis is applied to idealized neural systems that differ in the organization of their connections. The results indicate that Φ is maximized by having each element develop a different connection pattern with the rest of the complex (functional specialization) while ensuring that a large amount of information can be exchanged across any bipartition of the network (functional integration). CONCLUSION: Based on this analysis, the connectional organization of certain neural architectures, such as the thalamocortical system, are well suited to information integration, while that of others, such as the cerebellum, are not, with significant functional consequences. The proposed analysis of information integration should be applicable to other systems and networks

An empirical evaluation of computational and perceptual multi-label genre classification on music / Christopher Sanden

viii, 87 leaves ; 29 cmAutomatic music genre classi cation is a high-level task in the eld of Music Information Retrieval (MIR). It refers to the process of automatically assigning genre labels to music for various tasks, including, but not limited to categorization, organization and browsing. This is a topic which has seen an increase in interest recently as one of the cornerstones of MIR. However, due to the subjective and ambiguous nature of music, traditional single-label classi cation is inadequate. In this thesis, we study multi-label music genre classi cation from perceptual and computational perspectives. First, we design a set of perceptual experiments to investigate the genre-labelling behavior of individuals. The results from these experiments lead us to speculate that multi-label classi cation is more appropriate for classifying music genres. Second, we design a set of computational experiments to evaluate multi-label classi cation algorithms on music. These experiments not only support our speculation but also reveal which algorithms are more suitable for music genre classi cation. Finally, we propose and examine a group of ensemble approaches for combining multi-label classi cation algorithms to further improve classi cation performance. i

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

IMPORTANCE-DRIVEN TRANSFER FUNCTION DESIGN FOR VOLUME VISUALIZATION OF MEDICAL IMAGES

Ph.DDOCTOR OF PHILOSOPH

An Information Theoretic Approach For Feature Selection And Segmentation In Posterior Fossa Tumors

Posterior Fossa (PF) is a type of brain tumor located in or near brain stem and cerebellum. About 55% - 70 % pediatric brain tumors arise in the posterior fossa, compared with only 15% - 20% of adult tumors. For segmenting PF tumors we should have features to study the characteristics of tumors. In literature, different types of texture features such as Fractal Dimension (FD) and Multifractional Brownian Motion (mBm) have been exploited for measuring randomness associated with brain and tumor tissues structures, and the varying appearance of tissues in magnetic resonance images (MRI). For selecting best features techniques such as neural network and boosting methods have been exploited. However, neural network cannot descirbe about the properties of texture features. We explore methods such as information theroetic methods which can perform feature selection based on properties of texture features. The primary contribution of this dissertation is investigating efficacy of different image features such as intensity, fractal texture, and level - set shape in segmentation of PF tumor for pediatric patients. We explore effectiveness of using four different feature selection and three different segmentation techniques respectively to discriminate tumor regions from normal tissue in multimodal brain MRI. Our research suggest that Kullback - Leibler Divergence (KLD) measure for feature ranking and selection and Expectation Maximization (EM) algorithm for feature fusion and tumor segmentation offer the best performance for the patient data in this study. To improve segmentation accuracy, we need to consider abnormalities such as cyst, edema and necrosis which surround tumors. In this work, we exploit features which describe properties of cyst and technique which can be used to segment it. To achieve this goal, we extend the two class KLD techniques to multiclass feature selection techniques, so that we can effectively select features for tumor, cyst and non tumor tissues. We compute segemntation accuracy by computing number of pixels segemented to total number of pixels for the best features. For automated process we integrate the inhomoheneity correction, feature selection using KLD and segmentation in an integrated EM framework. To validate results we have used similarity coefficients for computing the robustness of segmented tumor and cyst

Typological parameters of genericity

Different languages employ different morphosyntactic devices for expressing genericity. And, of course, they also make use of different morphosyntactic and semantic or pragmatic cues which may contribute to the interpretation of a sentence as generic rather than episodic. [...] We will advance the strong hypo thesis that it is a fundamental property of lexical elements in natural language that they are neutral with respect to different modes of reference or non-reference. That is, we reject the idea that a certain use of a lexical element, e.g. a use which allows reference to particular spatio-temporally bounded objects in the world, should be linguistically prior to all other possible uses, e.g. to generic and non-specific uses. From this it follows that we do not consider generic uses as derived from non-generic uses as it is occasionally assumed in the literature. Rather, we regard these two possibilities of use as equivalent alternative uses of lexical elements. The typological differences to be noted therefore concern the formal and semantic relationship of generic and non-generic uses to each other; they do not pertain to the question of whether lexical elements are predetermined for one of these two uses. Even supposing we found a language where generic uses are always zero-marked and identical to lexical sterns, we would still not assume that lexical elements in this language primarily have a generic use from which the non-generic uses are derived. (Incidentally, none of the languages examined, not even Vietnamese, meets this criterion.