Bayesian Approach to Infer Types of Faults on Electrical Machines from Acoustic Signal

Considering the classification of failures in electrical machines, the present paper aims to use supervised machine learning techniques in order to classify faults in electrical machines, using attributes from audio signals. In order to analyze data and recognize patterns, the considered supervised learning methods are: Bayesian Network, together with the BayesRule algorithm, Support Vector Machine and k-Nearest Neighbor. The performances and the results provided from these algorithms are then compared. The main contributions of this paper are the acquisition process of audio signals and the elaboration of Bayesian networks topologies and classifiers structures using the acquired signals, since the algorithms provide the generalization of the classification model by revealing the network structure. Also, the utilization of audio signals as input attributes to the classifiers is infrequent in the literature. The results show that the Support Vector Machine and k-Nearest Neighbor present a high accuracy. On the other hand, the Bayesian approach is advantageous due to the possibility of showing, through graph representations, the generalized structure to represent the trend of faults in real cases on industry applications.info:eu-repo/semantics/publishedVersio

A Bayesian Rule Generation Framework for 'Omic' Biomedical Data Analysis

High-dimensional biomedical 'omic' datasets are accumulating rapidly from studies aimed at early detection and better management of human disease. These datasets pose tremendous challenges for analysis due to their large number of variables that represent measurements of biochemical molecules, such as proteins and mRNA, from bodily fluids or tissues extracted from a rather small cohort of samples. Machine learning methods have been applied to modeling these datasets including rule learning methods, which have been successful in generating models that are easily interpretable by the scientists. Rule learning methods have typically relied on a frequentist measure of certainty within IF-THEN (propositional) rules. In this dissertation, a Bayesian Rule Generation Framework (BRGF) is developed and tested that can produce rules with probabilities, thereby enabling a mathematically rigorous representation of uncertainty in rule models. The BRGF includes a novel Bayesian Discretization method combined with one or more search strategies for building constrained Bayesian Networks from data and converting them into probabilistic rules. Both global and local structures are built using different Bayesian Network generation algorithms and the rule models generated from the network are tested on public and private 'omic' datasets. We show that using a specific type of structure (Bayesian decision graphs) in tandem with a specific type of search method (parallel greedy) allows us to achieve statistically significant higher overall performance over current state of the art rule learning methods. Not only does using the BRGF boost performance on average on 'omic' biomedical data to a statistically significant point, but also provides the ability to incorporate prior information in a mathematically rigorous fashion for modeling purposes

Facilitating and Enhancing Biomedical Knowledge Translation: An in Silico Approach to Patient-centered Pharmacogenomic Outcomes Research

Current research paradigms such as traditional randomized control trials mostly rely on relatively narrow efficacy data which results in high internal validity and low external validity. Given this fact and the need to address many complex real-world healthcare questions in short periods of time, alternative research designs and approaches should be considered in translational research. In silico modeling studies, along with longitudinal observational studies, are considered as appropriate feasible means to address the slow pace of translational research. Taking into consideration this fact, there is a need for an approach that tests newly discovered genetic tests, via an in silico enhanced translational research model (iS-TR) to conduct patient-centered outcomes research and comparative effectiveness research studies (PCOR CER). In this dissertation, it was hypothesized that retrospective EMR analysis and subsequent mathematical modeling and simulation prediction could facilitate and accelerate the process of generating and translating pharmacogenomic knowledge on comparative effectiveness of anticoagulation treatment plan(s) tailored to well defined target populations which eventually results in a decrease in overall adverse risk and improve individual and population outcomes. To test this hypothesis, a simulation modeling framework (iS-TR) was proposed which takes advantage of the value of longitudinal electronic medical records (EMRs) to provide an effective approach to translate pharmacogenomic anticoagulation knowledge and conduct PCOR CER studies. The accuracy of the model was demonstrated by reproducing the outcomes of two major randomized clinical trials for individualizing warfarin dosing. A substantial, hospital healthcare use case that demonstrates the value of iS-TR when addressing real world anticoagulation PCOR CER challenges was also presented

Bayesian networks for omics data analysis

This thesis focuses on two aspects of high throughput technologies, i.e. data storage and data analysis, in particular in transcriptomics and metabolomics. Both technologies are part of a research field that is generally called ‘omics’ (or ‘-omics’, with a leading hyphen), which refers to genomics, transcriptomics, proteomics, or metabolomics. Although these techniques study different entities (genes, gene expression, proteins, or metabolites), they all have in common that they use high-throughput technologies such as microarrays and mass spectrometry, and thus generate huge amounts of data. Experiments conducted using these technologies allow one to compare different states of a living cell, for example a healthy cell versus a cancer cell or the effect of food on cell condition, and at different levels. The tools needed to apply omics technologies, in particular microarrays, are often manufactured by different vendors and require separate storage and analysis software for the data generated by them. Moreover experiments conducted using different technologies cannot be analyzed simultaneously to answer a biological question. Chapter 3 presents MADMAX, our software system which supports storage and analysis of data from multiple microarray platforms. It consists of a vendor-independent database which is tightly coupled with vendor-specific analysis tools. Upcoming technologies like metabolomics, proteomics and high-throughput sequencing can easily be incorporated in this system. Once the data are stored in this system, one obviously wants to deduce a biological relevant meaning from these data and here statistical and machine learning techniques play a key role. The aim of such analysis is to search for relationships between entities of interest, such as genes, metabolites or proteins. One of the major goals of these techniques is to search for causal relationships rather than mere correlations. It is often emphasized in the literature that "correlation is not causation" because people tend to jump to conclusions by making inferences about causal relationships when they actually only see correlations. Statistics are often good in finding these correlations; techniques called linear regression and analysis of variance form the core of applied multivariate statistics. However, these techniques cannot find causal relationships, neither are they able to incorporate prior knowledge of the biological domain. Graphical models, a machine learning technique, on the other hand do not suffer from these limitations. Graphical models, a combination of graph theory, statistics and information science, are one of the most exciting things happening today in the field of machine learning applied to biological problems (see chapter 2 for a general introduction). This thesis deals with a special type of graphical models known as probabilistic graphical models, belief networks or Bayesian networks. The advantage of Bayesian networks over classical statistical techniques is that they allow the incorporation of background knowledge from a biological domain, and that analysis of data is intuitive as it is represented in the form of graphs (nodes and edges). Standard statistical techniques are good in describing the data but are not able to find non-linear relations whereas Bayesian networks allow future prediction and discovering nonlinear relations. Moreover, Bayesian networks allow hierarchical representation of data, which makes them particularly useful for representing biological data, since most biological processes are hierarchical by nature. Once we have such a causal graph made either by a computer program or constructed manually we can predict the effects of a certain entity by manipulating the state of other entities, or make backward inferences from effects to causes. Of course, if the graph is big, doing the necessary calculations can be very difficult and CPU-expensive, and in such cases approximate methods are used. Chapter 4 demonstrates the use of Bayesian networks to determine the metabolic state of feeding and fasting mice to determine the effect of a high fat diet on gene expression. This chapter also shows how selection of genes based on key biological processes generates more informative results than standard statistical tests. In chapter 5 the use of Bayesian networks is shown on the combination of gene expression data and clinical parameters, to determine the effect of smoking on gene expression and which genes are responsible for the DNA damage and the raise in plasma cotinine levels of blood of a smoking population. This study was conducted at Maastricht University where 22 twin smokers were profiled. Chapter 6 presents the reconstruction of a key metabolic pathway which plays an important role in ripening of tomatoes, thus showing the versatility of the use of Bayesian networks in metabolomics data analysis. The general trend in research shows a flood of data emerging from sequencing and metabolomics experiments. This means that to perform data mining on these data one requires intelligent techniques that are computationally feasible and able to take the knowledge of experts into account to generate relevant results. Graphical models fit this paradigm well and we expect them to play a key role in mining the data generated from omics experiments. <br/

ALGORITHMS FOR CONSTRAINT-BASED LEARNING OF BAYESIAN NETWORK STRUCTURES WITH LARGE NUMBERS OF VARIABLES

Bayesian networks (BNs) are highly practical and successful tools for modeling probabilistic knowledge. They can be constructed by an expert, learned from data, or by a combination of the two. A popular approach to learning the structure of a BN is the constraint-based search (CBS) approach, with the PC algorithm being a prominent example. In recent years, we have been experiencing a data deluge. We have access to more data, big and small, than ever before. The exponential nature of BN algorithms, however, hinders large-scale analysis. Developments in parallel and distributed computing have made the computational power required for large-scale data processing widely available, yielding opportunities for developing parallel and distributed algorithms for BN learning and inference. In this dissertation, (1) I propose two MapReduce versions of the PC algorithm, aimed at solving an increasingly common case: data is not necessarily massive in the number of records, but more and more so in the number of variables. (2) When the number of data records is small, the PC algorithm experiences problems in independence testing. Empirically, I explore a contradiction in the literature on how to resolve the case of having insufficient data when testing the independence of two variables: declare independence or dependence. (3) When BNs learned from data become complex in terms of graph density, they may require more parameters than we can feasibly store. I propose and evaluate five approaches to pruning a BN structure to guarantee that it will be tractable for storage and inference. I follow this up by proposing three approaches to improving the classification accuracy of a BN by modifying its structure

ADVANCES IN IMPROVING SCALABILITY AND ACCURACY OF MLNS USING SYMMETRIES

ADVANCES IN IMPROVING SCALABILITY AND ACCURACY OF MLNS USING SYMMETRIE

Discovering robust dependencies from data

Science revolves around forming hypotheses, designing experiments, collecting data, and tests. It was not until recently, with the advent of modern hardware and data analytics, that science shifted towards a big-data-driven paradigm that led to an unprecedented success across various fields. What is perhaps the most astounding feature of this new era, is that interesting hypotheses can now be automatically discovered from observational data. This dissertation investigates knowledge discovery procedures that do exactly this. In particular, we seek algorithms that discover the most informative models able to compactly “describe” aspects of the phenomena under investigation, in both supervised and unsupervised settings. We consider interpretable models in the form of subsets of the original variable set. We want the models to capture all possible interactions, e.g., linear, non-linear, between all types of variables, e.g., discrete, continuous, and lastly, we want their quality to be meaningfully assessed. For this, we employ information-theoretic measures, and particularly, the fraction of information for the supervised setting, and the normalized total correlation for the unsupervised. The former measures the uncertainty reduction of the target variable conditioned on a model, and the latter measures the information overlap of the variables included in a model. Without access to the true underlying data generating process, we estimate the aforementioned measures from observational data. This process is prone to statistical errors, and in our case, the errors manifest as biases towards larger models. This can lead to situations where the results are utterly random, hindering therefore further analysis. We correct this behavior with notions from statistical learning theory. In particular, we propose regularized estimators that are unbiased under the hypothesis of independence, leading to robust estimation from limited data samples and arbitrary dimensionalities. Moreover, we do this for models consisting of both discrete and continuous variables. Lastly, to discover the top scoring models, we derive effective optimization algorithms for exact, approximate, and heuristic search. These algorithms are powered by admissible, tight, and efficient-to-compute bounding functions for our proposed estimators that can be used to greatly prune the search space. Overall, the products of this dissertation can successfully assist data analysts with data exploration, discovering powerful description models, or concluding that no satisfactory models exist, implying therefore new experiments and data are required for the phenomena under investigation. This statement is supported by Materials Science researchers who corroborated our discoveries.In der Wissenschaft geht es um Hypothesenbildung, Entwerfen von Experimenten, Sammeln von Daten und Tests. Jüngst hat sich die Wissenschaft, durch das Aufkommen moderner Hardware und Datenanalyse, zu einem Big-Data-basierten Paradigma hin entwickelt, das zu einem beispiellosen Erfolg in verschiedenen Bereichen geführt hat. Ein erstaunliches Merkmal dieser neuen ra ist, dass interessante Hypothesen jetzt automatisch aus Beobachtungsdaten entdeckt werden k nnen. In dieser Dissertation werden Verfahren zur Wissensentdeckung untersucht, die genau dies tun. Insbesondere suchen wir nach Algorithmen, die Modelle identifizieren, die in der Lage sind, Aspekte der untersuchten Ph nomene sowohl in beaufsichtigten als auch in unbeaufsichtigten Szenarien kompakt zu “beschreiben”. Hierzu betrachten wir interpretierbare Modelle in Form von Untermengen der ursprünglichen Variablenmenge. Ziel ist es, dass diese Modelle alle m glichen Interaktionen erfassen (z.B. linear, nicht-lineare), zwischen allen Arten von Variablen unterscheiden (z.B. diskrete, kontinuierliche) und dass schlussendlich ihre Qualit t sinnvoll bewertet wird. Dazu setzen wir informationstheoretische Ma e ein, insbesondere den Informationsanteil für das überwachte und die normalisierte Gesamtkorrelation für das unüberwachte Szenario. Ersteres misst die Unsicherheitsreduktion der Zielvariablen, die durch ein Modell bedingt ist, und letztere misst die Informationsüberlappung der enthaltenen Variablen. Ohne Kontrolle des Datengenerierungsprozesses werden die oben genannten Ma e aus Beobachtungsdaten gesch tzt. Dies ist anf llig für statistische Fehler, die zu Verzerrungen in gr  eren Modellen führen. So entstehen Situationen, wobei die Ergebnisse v llig zuf llig sind und somit weitere Analysen st ren. Wir korrigieren dieses Verhalten mit Methoden aus der statistischen Lerntheorie. Insbesondere schlagen wir regularisierte Sch tzer vor, die unter der Hypothese der Unabh ngigkeit nicht verzerrt sind und somit zu einer robusten Sch tzung aus begrenzten Datenstichproben und willkürlichen-Dimensionalit ten führen. Darüber hinaus wenden wir dies für Modelle an, die sowohl aus diskreten als auch aus kontinuierlichen Variablen bestehen. Um die besten Modelle zu entdecken, leiten wir effektive Optimierungsalgorithmen mit verschiedenen Garantien ab. Diese Algorithmen basieren auf speziellen Begrenzungsfunktionen der vorgeschlagenen Sch tzer und erlauben es den Suchraum stark einzuschr nken. Insgesamt sind die Produkte dieser Arbeit sehr effektiv für die Wissensentdeckung. Letztere Aussage wurde von Materialwissenschaftlern best tigt

Scalable statistical learning for relation prediction on structured data

Relation prediction seeks to predict unknown but potentially true relations by revealing missing relations in available data, by predicting future events based on historical data, and by making predicted relations retrievable by query. The approach developed in this thesis can be used for a wide variety of purposes, including to predict likely new friends on social networks, attractive points of interest for an individual visiting an unfamiliar city, and associations between genes and particular diseases. In recent years, relation prediction has attracted significant interest in both research and application domains, partially due to the increasing volume of published structured data and background knowledge. In the Linked Open Data initiative of the Semantic Web, for instance, entities are uniquely identified such that the published information can be integrated into applications and services, and the rapid increase in the availability of such structured data creates excellent opportunities as well as challenges for relation prediction. This thesis focuses on the prediction of potential relations by exploiting regularities in data using statistical relational learning algorithms and applying these methods to relational knowledge bases, in particular in Linked Open Data in particular. We review representative statistical relational learning approaches, e.g., Inductive Logic Programming and Probabilistic Relational Models. While logic-based reasoning can infer and include new relations via deduction by using ontologies, machine learning can be exploited to predict new relations (with some degree of certainty) via induction, purely based on the data. Because the application of machine learning approaches to relation prediction usually requires handling large datasets, we also discuss the scalability of machine learning as a solution to relation prediction, as well as the significant challenge posed by incomplete relational data (such as social network data, which is often much more extensive for some users than others). The main contribution of this thesis is to develop a learning framework called the Statistical Unit Node Set (SUNS) and to propose a multivariate prediction approach used in the framework. We argue that multivariate prediction approaches are most suitable for dealing with large, sparse data matrices. According to the characteristics and intended application of the data, the approach can be extended in different ways. We discuss and test two extensions of the approach--kernelization and a probabilistic method of handling complex n-ary relationships--in empirical studies based on real-world data sets. Additionally, this thesis contributes to the field of relation prediction by applying the SUNS framework to various domains. We focus on three applications: 1. In social network analysis, we present a combined approach of inductive and deductive reasoning for recommending movies to users. 2. In the life sciences, we address the disease gene prioritization problem. 3. In the recommendation system, we describe and investigate the back-end of a mobile app called BOTTARI, which provides personalized location-based recommendations of restaurants.Die Beziehungsvorhersage strebt an, unbekannte aber potenziell wahre Beziehungen vorherzusagen, indem fehlende Relationen in verfügbaren Daten aufgedeckt, zukünftige Ereignisse auf der Grundlage historischer Daten prognostiziert und vorhergesagte Relationen durch Anfragen abrufbar gemacht werden. Der in dieser Arbeit entwickelte Ansatz lässt sich für eine Vielzahl von Zwecken einschließlich der Vorhersage wahrscheinlicher neuer Freunde in sozialen Netzen, der Empfehlung attraktiver Sehenswürdigkeiten für Touristen in fremden Städten und der Priorisierung möglicher Assoziationen zwischen Genen und bestimmten Krankheiten, verwenden. In den letzten Jahren hat die Beziehungsvorhersage sowohl in Forschungs- als auch in Anwendungsbereichen eine enorme Aufmerksamkeit erregt, aufgrund des Zuwachses veröffentlichter strukturierter Daten und von Hintergrundwissen. In der Linked Open Data-Initiative des Semantischen Web werden beispielsweise Entitäten eindeutig identifiziert, sodass die veröffentlichten Informationen in Anwendungen und Dienste integriert werden können. Diese rapide Erhöhung der Verfügbarkeit strukturierter Daten bietet hervorragende Gelegenheiten sowie Herausforderungen für die Beziehungsvorhersage. Diese Arbeit fokussiert sich auf die Vorhersage potenzieller Beziehungen durch Ausnutzung von Regelmäßigkeiten in Daten unter der Verwendung statistischer relationaler Lernalgorithmen und durch Einsatz dieser Methoden in relationale Wissensbasen, insbesondere in den Linked Open Daten. Wir geben einen Überblick über repräsentative statistische relationale Lernansätze, z.B. die Induktive Logikprogrammierung und Probabilistische Relationale Modelle. Während das logikbasierte Reasoning neue Beziehungen unter der Nutzung von Ontologien ableiten und diese einbeziehen kann, kann maschinelles Lernen neue Beziehungen (mit gewisser Wahrscheinlichkeit) durch Induktion ausschließlich auf der Basis der vorliegenden Daten vorhersagen. Da die Verarbeitung von massiven Datenmengen in der Regel erforderlich ist, wenn maschinelle Lernmethoden in die Beziehungsvorhersage eingesetzt werden, diskutieren wir auch die Skalierbarkeit des maschinellen Lernens sowie die erhebliche Herausforderung, die sich aus unvollständigen relationalen Daten ergibt (z. B. Daten aus sozialen Netzen, die oft für manche Benutzer wesentlich umfangreicher sind als für Anderen). Der Hauptbeitrag der vorliegenden Arbeit besteht darin, ein Lernframework namens Statistical Unit Node Set (SUNS) zu entwickeln und einen im Framework angewendeten multivariaten Prädiktionsansatz einzubringen. Wir argumentieren, dass multivariate Vorhersageansätze am besten für die Bearbeitung von großen und dünnbesetzten Datenmatrizen geeignet sind. Je nach den Eigenschaften und der beabsichtigten Anwendung der Daten kann der Ansatz auf verschiedene Weise erweitert werden. In empirischen Studien werden zwei Erweiterungen des Ansatzes--ein kernelisierter Ansatz sowie ein probabilistischer Ansatz zur Behandlung komplexer n-stelliger Beziehungen-- diskutiert und auf realen Datensätzen untersucht. Ein weiterer Beitrag dieser Arbeit ist die Anwendung des SUNS Frameworks auf verschiedene Bereiche. Wir konzentrieren uns auf drei Anwendungen: 1. In der Analyse sozialer Netze stellen wir einen kombinierten Ansatz von induktivem und deduktivem Reasoning vor, um Benutzern Filme zu empfehlen. 2. In den Biowissenschaften befassen wir uns mit dem Problem der Priorisierung von Krankheitsgenen. 3. In den Empfehlungssystemen beschreiben und untersuchen wir das Backend einer mobilen App "BOTTARI", das personalisierte ortsbezogene Empfehlungen von Restaurants bietet