Time-series representation framework based on multi-instance similarity measures

Time series analysis plays an essential role in today’s society due to the ease of access to information. This analysis is present in the majority of applications that involve sensors, but in recent years thanks to technological advancement, this approach has been directed towards the treatment of complex signals that lack periodicity and even that present non-stationary dynamics such as signals of brain activity or magnetic and satellite resonance images. The main challenges at the time of time series analysis are focused on the representation of the same, for which methodologies based on similarity measures have been proposed. However, these approaches are oriented to the measurement of local patterns point-to-point in the signals using metrics based on the form. Besides, the selection of relevant information from the representations is of high importance, in order to eliminate noise and train classifiers with discriminant information for the analysis tasks, however, this selection is usually made at the level of characteristics, leaving aside the Global signal information. In the same way, lately, there have been applications in which it is necessary to analyze time series from different sources of information or multimodal, for which there are methods that generate acceptable performance but lack interpretability. In this regard, we propose a framework based on representations of similarity and multiple-instance learning that allows selecting relevant information for classification tasks in order to improve the performance and interpretability of the modelsResumen: El análisis de series de tiempo juega un papel importante en la sociedad actual debido a la facilidad de acceso a la información. Este análisis está presente en la mayoría de aplicaciones que involucran sensores, pero en los ´últimos años gracias al avance tecnológico, este enfoque se ha encaminado hacia el tratamiento de señales complejas que carecen de periodicidad e incluso que presentan dinámicas no estacionarias como lo son las señales de actividad cerebral o las imágenes de resonancias magnéticas y satelitales. Los principales retos a la hora de realizar en análisis de series de tiempo se centran en la representación de las mismas, para lo cual se han propuesto metodologías basadas en medidas de similitud, sin embargo, estos enfoques están orientados a la medición de patrones locales punto a punto en las señales utilizando métricas basadas en la forma. Además, es de alta importancia la selección de información relevante de las representaciones, con el fin de eliminar el ruido y entrenar clasificadores con información discriminante para las tareas de análisis, sin embargo, esta selección se suele hacer a nivel de características, dejando de lado la información de global de la señal. De la misma manera, ´últimamente han surgido aplicaciones en las cuales es necesario el análisis de series de tiempo provenientes de diferentes fuentes de información o multimodales, para lo cual existen métodos que generan un rendimiento aceptable, pero carecen de interpretabilidad. En este sentido, en nosotros proponemos un marco de trabajo basado en representaciones de similitud y aprendizaje de múltiples instancias que permita seleccionar información relevante para tareas de clasificación con el fin de mejorar el rendimiento y la interpretabilidad de los modelosMaestrí

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Multi-Label Learning under Feature Extraction Budgets

We consider the problem of learning sparse linear models for multi-label prediction tasks under a hard constraint on the number of features. Such budget constraints are important in domains where the acquisition of the feature values is costly. We propose a greedy multi-label regularized least-squares algorithm that solves this problem by combining greedy forward selection search with a cross-validation based selection criterion in order to choose, which features to include in the model. We present a highly efficient algorithm for implementing this procedure with linear time and space complexities. This is achieved through the use of matrix update formulas for speeding up feature addition and cross-validation computations. Experimentally, we demonstrate that the approach allows finding sparse accurate predictors on a wide range of benchmark problems, typically outperforming the multi-task lasso baseline method when the budget is small.</p

The Emerging Trends of Multi-Label Learning

Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202

A framework for applying natural language processing in digital health interventions

BACKGROUND: Digital health interventions (DHIs) are poised to reduce target symptoms in a scalable, affordable, and empirically supported way. DHIs that involve coaching or clinical support often collect text data from 2 sources: (1) open correspondence between users and the trained practitioners supporting them through a messaging system and (2) text data recorded during the intervention by users, such as diary entries. Natural language processing (NLP) offers methods for analyzing text, augmenting the understanding of intervention effects, and informing therapeutic decision making. OBJECTIVE: This study aimed to present a technical framework that supports the automated analysis of both types of text data often present in DHIs. This framework generates text features and helps to build statistical models to predict target variables, including user engagement, symptom change, and therapeutic outcomes. METHODS: We first discussed various NLP techniques and demonstrated how they are implemented in the presented framework. We then applied the framework in a case study of the Healthy Body Image Program, a Web-based intervention trial for eating disorders (EDs). A total of 372 participants who screened positive for an ED received a DHI aimed at reducing ED psychopathology (including binge eating and purging behaviors) and improving body image. These users generated 37,228 intervention text snippets and exchanged 4285 user-coach messages, which were analyzed using the proposed model. RESULTS: We applied the framework to predict binge eating behavior, resulting in an area under the curve between 0.57 (when applied to new users) and 0.72 (when applied to new symptom reports of known users). In addition, initial evidence indicated that specific text features predicted the therapeutic outcome of reducing ED symptoms. CONCLUSIONS: The case study demonstrates the usefulness of a structured approach to text data analytics. NLP techniques improve the prediction of symptom changes in DHIs. We present a technical framework that can be easily applied in other clinical trials and clinical presentations and encourage other groups to apply the framework in similar contexts

Multi-Objective Genetic Algorithm for Multi-View Feature Selection

Multi-view datasets offer diverse forms of data that can enhance prediction models by providing complementary information. However, the use of multi-view data leads to an increase in high-dimensional data, which poses significant challenges for the prediction models that can lead to poor generalization. Therefore, relevant feature selection from multi-view datasets is important as it not only addresses the poor generalization but also enhances the interpretability of the models. Despite the success of traditional feature selection methods, they have limitations in leveraging intrinsic information across modalities, lacking generalizability, and being tailored to specific classification tasks. We propose a novel genetic algorithm strategy to overcome these limitations of traditional feature selection methods for multi-view data. Our proposed approach, called the multi-view multi-objective feature selection genetic algorithm (MMFS-GA), simultaneously selects the optimal subset of features within a view and between views under a unified framework. The MMFS-GA framework demonstrates superior performance and interpretability for feature selection on multi-view datasets in both binary and multiclass classification tasks. The results of our evaluations on three benchmark datasets, including synthetic and real data, show improvement over the best baseline methods. This work provides a promising solution for multi-view feature selection and opens up new possibilities for further research in multi-view datasets

Sparse Predictive Modeling : A Cost-Effective Perspective

Many real life problems encountered in industry, economics or engineering are complex and difficult to model by conventional mathematical methods. Machine learning provides a wide variety of methods and tools for solving such problems by learning mathematical models from data. Methods from the field have found their way to applications such as medical diagnosis, financial forecasting, and web-search engines. The predictions made by a learned model are based on a vector of feature values describing the input to the model. However, predictions do not come for free in real world applications, since the feature values of the input have to be bought, measured or produced before the model can be used. Feature selection is a process of eliminating irrelevant and redundant features from the model. Traditionally, it has been applied for achieving interpretable and more accurate models, while the possibility of lowering prediction costs has received much less attention in the literature. In this thesis we consider novel feature selection techniques for reducing prediction costs. The contributions of this thesis are as follows. First, we propose several cost types characterizing the cost of performing prediction with a trained model. Particularly, we consider costs emerging from multitarget prediction problems as well as a number of cost types arising when the feature extraction process is structured. Second, we develop greedy regularized least-squares methods to maximize the predictive performance of the models under given budget constraints. Empirical evaluations are performed on numerous benchmark data sets as well as on a novel water quality analysis application. The results demonstrate that in settings where the considered cost types apply, the proposed methods lead to substantial cost savings compared to conventional methods

Sensing Human Sentiment via Social Media Images: Methodologies and Applications

abstract: Social media refers computer-based technology that allows the sharing of information and building the virtual networks and communities. With the development of internet based services and applications, user can engage with social media via computer and smart mobile devices. In recent years, social media has taken the form of different activities such as social network, business network, text sharing, photo sharing, blogging, etc. With the increasing popularity of social media, it has accumulated a large amount of data which enables understanding the human behavior possible. Compared with traditional survey based methods, the analysis of social media provides us a golden opportunity to understand individuals at scale and in turn allows us to design better services that can tailor to individuals’ needs. From this perspective, we can view social media as sensors, which provides online signals from a virtual world that has no geographical boundaries for the real world individual's activity. One of the key features for social media is social, where social media users actively interact to each via generating content and expressing the opinions, such as post and comment in Facebook. As a result, sentiment analysis, which refers a computational model to identify, extract or characterize subjective information expressed in a given piece of text, has successfully employs user signals and brings many real world applications in different domains such as e-commerce, politics, marketing, etc. The goal of sentiment analysis is to classify a user’s attitude towards various topics into positive, negative or neutral categories based on textual data in social media. However, recently, there is an increasing number of people start to use photos to express their daily life on social media platforms like Flickr and Instagram. Therefore, analyzing the sentiment from visual data is poise to have great improvement for user understanding. In this dissertation, I study the problem of understanding human sentiments from large scale collection of social images based on both image features and contextual social network features. We show that neither visual features nor the textual features are by themselves sufficient for accurate sentiment prediction. Therefore, we provide a way of using both of them, and formulate sentiment prediction problem in two scenarios: supervised and unsupervised. We first show that the proposed framework has flexibility to incorporate multiple modalities of information and has the capability to learn from heterogeneous features jointly with sufficient training data. Secondly, we observe that negative sentiment may related to human mental health issues. Based on this observation, we aim to understand the negative social media posts, especially the post related to depression e.g., self-harm content. Our analysis, the first of its kind, reveals a number of important findings. Thirdly, we extend the proposed sentiment prediction task to a general multi-label visual recognition task to demonstrate the methodology flexibility behind our sentiment analysis model.Dissertation/ThesisDoctoral Dissertation Computer Science 201