A survey on online active learning

Online active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in the context of online active learning. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research. Our review aims to provide a comprehensive and up-to-date overview of the field and to highlight directions for future work

Indexing and knowledge discovery of gaussian mixture models and multiple-instance learning

Due to the increasing quantity and variety of generated and stored data, the manual and automatic analysis becomes a more and more challenging task in many modern applications, like biometric identification and content-based image retrieval. In this thesis, we consider two very typical, related inherent structures of objects: Multiple-Instance (MI) objects and Gaussian Mixture Models (GMM). In both approaches, each object is represented by a set. For MI, each object is a set of vectors from a multi-dimensional space. For GMM, each object is a set of multi-variate Gaussian distribution functions, providing the ability to approximate arbitrary distributions in a concise way. Both approaches are very powerful and natural as they allow to express (1) that an object is additively composed from several components or (2) that an object may have several different, alternative kinds of behavior. Thus we can model e.g. an image which may depict a set of different things (1). Likewise, we can model a sports player who has performed differently at different games (2). We can use GMM to approximate MI objects and vice versa. Both ways of approximation can be appealing because GMM are more concise whereas for MI objects the single components are less complex. A similarity measure quantifies similarities between two objects to assess how much alike these objects are. On this basis, indexing and similarity search play essential roles in data mining, providing efficient and/or indispensable supports for a variety of algorithms such as classification and clustering. This thesis aims to solve challenges in the indexing and knowledge discovery of complex data using MI objects and GMM. For the indexing of GMM, there are several techniques available, including universal index structures and GMM-specific methods. However, the well-known approaches either suffer from poor performance or have too many limitations. To make use of the parameterized properties of GMM and tackle the problem of potential unequal length of components, we propose the Gaussian Components based Index (GCI) for efficient queries on GMM. GCI decomposes GMM into their components, and stores the n-lets of Gaussian combinations that have uniform length of parameter vectors in traditional index structures. We introduce an efficient pruning strategy to filter unqualified GMM using the so-called Matching Probability (MP) as the similarity measure. MP sums up the joint probabilities of two objects all over the space. GCI achieves better performance than its competitors on both synthetic and real-world data. To further increase its efficiency, we propose a strategy to store GMM components in a normalized way. This strategy improves the ability of filtering unqualified GMM. Based on the normalized transformation, we derive a set of novel similarity measures for GMM. Since MP is not a metric (i.e., a symmetric, positive definite distance function guaranteeing the triangle inequality), which would be essential for the application of various analysis techniques, we introduce Infinite Euclidean Distance (IED) for probability distribution functions, a metric with a closed-form expression for GMM. IED allows us to store GMM in well-known metric trees like the Vantage-Point tree or M-tree, which facilitate similarity search in sublinear time by exploiting the triangle inequality. Moreover, analysis techniques that require the properties of a metric (e.g. Multidimensional Scaling) can be applied on GMM with IED. For MI objects which are not well-approximated by GMM, we introduce the potential densities of instances for the representation of MI objects. Based on that, two joint Gaussian based measures are proposed for MI objects and we extend GCI on MI objects for efficient queries as well. To sum up, we propose in this thesis a number of novel similarity measures and novel indexing techniques for GMM and MI objects, enabling efficient queries and knowledge discovery on complex data. In a thorough theoretic analysis as well as extensive experiments we demonstrate the superiority of our approaches over the state-of-the-art with respect to the run-time efficiency and the quality of the result.Angesichts der steigenden Quantität und Vielfalt der generierten und gespeicherten Daten werden manuelle und automatisierte Analysen in vielen modernen Anwendungen eine zunehmend anspruchsvolle Aufgabe, wie z.B. biometrische Identifikation und inhaltbasierter Bildzugriff. In dieser Arbeit werden zwei sehr typische und relevante inhärente Strukturen von Objekten behandelt: Multiple-Instance-Objects (MI) und Gaussian Mixture Models (GMM). In beiden Anwendungsfällen wird das Objekt in Form einer Menge dargestellt. Bei MI besteht jedes Objekt aus einer Menge von Vektoren aus einem multidimensionalen Raum. Bei GMM wird jedes Objekt durch eine Menge von multivariaten normalverteilten Dichtefunktionen repräsentiert. Dies bietet die Möglichkeit, beliebige Wahrscheinlichkeitsverteilungen in kompakter Form zu approximieren. Beide Ansätze sind sehr leistungsfähig, denn sie basieren auf einfachsten Ideen: (1) entweder besteht ein Objekt additiv aus mehreren Komponenten oder (2) ein Objekt hat unterschiedliche alternative Verhaltensarten. Dies ermöglicht es uns z.B. ein Bild zu repräsentieren, welches unterschiedliche Objekte und Szenen zeigt (1). In gleicher Weise können wir einen Sportler modellieren, der bei verschiedenen Wettkämpfen unterschiedliche Leistungen gezeigt hat (2). Wir können MI-Objekte durch GMM approximieren und auch der umgekehrte Weg ist möglich. Beide Vorgehensweisen können sehr ansprechend sein, da GMM im Vergleich zu MI kompakter sind, wogegen in MI-Objekten die einzelnen Komponenten weniger Komplexität aufweisen. Ein ähnlichkeitsmaß dient der Quantifikation der Gemeinsamkeit zwischen zwei Objekten. Darauf basierend spielen Indizierung und ähnlichkeitssuche eine wesentliche Rolle für die effiziente Implementierung von einer Vielzahl von Klassifikations- und Clustering-Algorithmen im Bereich des Data Minings. Ziel dieser Arbeit ist es, die Herausforderungen bei Indizierung und Wissensextraktion von komplexen Daten unter Verwendung von MI Objekten und GMM zu bewältigen. Für die Indizierung der GMM stehen verschiedene universelle und GMM-spezifische Indexstrukuren zur Verfügung. Jedoch leiden solche bekannten Ansätze unter schwacher Leistung oder zu vielen Einschränkungen. Um die parametrisieren Eigenschaften der GMM auszunutzen und dem Problem der möglichen ungleichen Komponentenlänge entgegenzuwirken, präsentieren wir das Verfahren Gaussian Components based Index (GCI), welches effizienten Abfrage auf GMM ermöglicht. GCI zerlegt dabei ein GMM in Parameterkomponenten und speichert alle möglichen Kombinationen mit einheitlicher Vektorlänge in traditionellen Indexstrukturen. Wir stellen ein effizientes Pruningverfahren vor, um ungeeignete GMM unter Verwendung der sogenannten Matching Probability (MP) als ähnlichkeitsma\ss auszufiltern. MP errechnet die Summe der gemeinsamen Wahrscheinlichkeit zweier Objekte aus dem gesamten Raum. CGI erzielt bessere Leistung als konkurrierende Verfahren, sowohl in Bezug auf synthetische, als auch auf reale Datensätze. Um ihre Effizienz weiter zu verbessern, stellen wir eine Strategie zur Speicherung der GMM-Komponenten in normalisierter Form vor. Diese Strategie verbessert die Fähigkeit zum Ausfiltern ungeeigneter GMM. Darüber hinaus leiten wir, basierend auf dieser Transformation, neuartige ähnlichkeitsmaße für GMM her. Da MP keine Metrik (d.h. eine symmetrische, positiv definite Distanzfunktion, die die Dreiecksungleichung garantiert) ist, dies jedoch unentbehrlich für die Anwendung mehrerer Analysetechniken ist, führen wir Infinite Euclidean Distance (IED) ein, ein Metrik mit geschlossener Ausdrucksform für GMM. IED erlaubt die Speicherung der GMM in Metrik-Bäumen wie z.B. Vantage-Point Trees oder M-Trees, die die ähnlichkeitssuche in sublinear Zeit mit Hilfe der Dreiecksungleichung erleichtert. Außerdem können Analysetechniken, die die Eigenschaften einer Metrik erfordern (z.B. Multidimensional Scaling), auf GMM mit IED angewandt werden. Für MI-Objekte, die mit GMM nicht in außreichender Qualität approximiert werden können, stellen wir Potential Densities of Instances vor, um MI-Objekte zu repräsentieren. Darauf beruhend werden zwei auf multivariater Gaußverteilungen basierende Maße für MI-Objekte eingeführt. Außerdem erweitern wir GCI für MI-Objekte zur effizienten Abfragen. Zusammenfassend haben wir in dieser Arbeit mehrere neuartige ähnlichkeitsmaße und Indizierungstechniken für GMM- und MI-Objekte vorgestellt. Diese ermöglichen effiziente Abfragen und die Wissensentdeckung in komplexen Daten. Durch eine gründliche theoretische Analyse und durch umfangreiche Experimente demonstrieren wir die überlegenheit unseres Ansatzes gegenüber anderen modernen Ansätzen bezüglich ihrer Laufzeit und Qualität der Resultate

Power System Stability Analysis using Neural Network

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Privacy preserving linkage and sharing of sensitive data

2018 Summer.Includes bibliographical references.Sensitive data, such as personal and business information, is collected by many service providers nowadays. This data is considered as a rich source of information for research purposes that could benet individuals, researchers and service providers. However, because of the sensitivity of such data, privacy concerns, legislations, and con ict of interests, data holders are reluctant to share their data with others. Data holders typically lter out or obliterate privacy related sensitive information from their data before sharing it, which limits the utility of this data and aects the accuracy of research. Such practice will protect individuals' privacy; however it prevents researchers from linking records belonging to the same individual across dierent sources. This is commonly referred to as record linkage problem by the healthcare industry. In this dissertation, our main focus is on designing and implementing ecient privacy preserving methods that will encourage sensitive information sources to share their data with researchers without compromising the privacy of the clients or aecting the quality of the research data. The proposed solution should be scalable and ecient for real-world deploy- ments and provide good privacy assurance. While this problem has been investigated before, most of the proposed solutions were either considered as partial solutions, not accurate, or impractical, and therefore subject to further improvements. We have identied several issues and limitations in the state of the art solutions and provided a number of contributions that improve upon existing solutions. Our rst contribution is the design of privacy preserving record linkage protocol using semi-trusted third party. The protocol allows a set of data publishers (data holders) who compete with each other, to share sensitive information with subscribers (researchers) while preserving the privacy of their clients and without sharing encryption keys. Our second contribution is the design and implementation of a probabilistic privacy preserving record linkage protocol, that accommodates discrepancies and errors in the data such as typos. This work builds upon the previous work by linking the records that are similar, where the similarity range is formally dened. Our third contribution is a protocol that performs information integration and sharing without third party services. We use garbled circuits secure computation to design and build a system to perform the record linkages between two parties without sharing their data. Our design uses Bloom lters as inputs to the garbled circuits and performs a probabilistic record linkage using the Dice coecient similarity measure. As garbled circuits are known for their expensive computations, we propose new approaches that reduce the computation overhead needed, to achieve a given level of privacy. We built a scalable record linkage system using garbled circuits, that could be deployed in a distributed computation environment like the cloud, and evaluated its security and performance. One of the performance issues for linking large datasets is the amount of secure computation to compare every pair of records across the linked datasets to nd all possible record matches. To reduce the amount of computations a method, known as blocking, is used to lter out as much as possible of the record pairs that will not match, and limit the comparison to a subset of the record pairs (called can- didate pairs) that possibly match. Most of the current blocking methods either require the parties to share blocking keys (called blocks identiers), extracted from the domain of some record attributes (termed blocking variables), or share reference data points to group their records around these points using some similarity measures. Though these methods reduce the computation substantially, they leak too much information about the records within each block. Toward this end, we proposed a novel privacy preserving approximate blocking scheme that allows parties to generate the list of candidate pairs with high accuracy, while protecting the privacy of the records in each block. Our scheme is congurable such that the level of performance and accuracy could be achieved according to the required level of privacy. We analyzed the accuracy and privacy of our scheme, implemented a prototype of the scheme, and experimentally evaluated its accuracy and performance against dierent levels of privacy

Fault Detection and Diagnosis Encyclopedia for Building Systems:A Systematic Review

This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository

Clustering: Methodology, hybrid systems, visualization, validation and implementation

Unsupervised learning is one of the most important steps of machine learning applications. Besides its ability to obtain the insight of the data distribution, unsupervised learning is used as a preprocessing step for other machine learning algorithm. This dissertation investigates the application of unsupervised learning into various types of data for many machine learning tasks such as clustering, regression and classification. The dissertation is organized into three papers. In the first paper, unsupervised learning is applied to mixed categorical and numerical feature data type to transform the data objects from the mixed type feature domain into a new sparser numerical domain. By making use of the data fusion capacity of adaptive resonance theory clustering, the approach is able to reduce the distinction between the numerical and categorical features. The second paper presents a novel method to improve the performance of wind forecast by clustering the time series of the surrounding wind mills into the similar group by using hidden Markov model clustering and using the clustering information to enhance the forecast. A fast forecast method is also introduced by using extreme learning machine which can be trained by analytic form to choose the optimal value of past samples for prediction and appropriate size of the neural network. In the third paper, unsupervised learning is used to automatically learn the feature from the dataset itself without human design of sophisticated feature extractors. The paper points out that by using unsupervised feature learning with multi-quadric radial basis function extreme learning machine the performance of the classifier is better than several other supervised learning methods. The paper further improves the speed of training the neural network by presenting an algorithm that runs parallel on GPU --Abstract, page iv

Applicability domains of neural networks for toxicity prediction

In this paper, the term "applicability domain" refers to the range of chemical compounds for which the statistical quantitative structure-activity relationship (QSAR) model can accurately predict their toxicity. This is a crucial concept in the development and practical use of these models. First, a multidisciplinary review is provided regarding the theory and practice of applicability domains in the context of toxicity problems using the classical QSAR model. Then, the advantages and improved performance of neural networks (NNs), which are the most promising machine learning algorithms, are reviewed. Within the domain of medicinal chemistry, nine different methods using NNs for toxicity prediction were compared utilizing 29 alternative artificial intelligence (AI) techniques. Similarly, seven NN-based toxicity prediction methodologies were compared to six other AI techniques within the realm of food safety, 11 NN-based methodologies were compared to 16 different AI approaches in the environmental sciences category and four specific NN-based toxicity prediction methodologies were compared to nine alternative AI techniques in the field of industrial hygiene. Within the reviewed approaches, given known toxic compound descriptors and behaviors, we observed a difficulty in being able to extrapolate and predict the effects with untested chemical compounds. Different methods can be used for unsupervised clustering, such as distance-based approaches and consensus-based decision methods. Additionally, the importance of model validation has been highlighted within a regulatory context according to the Organization for Economic Co-operation and Development (OECD) principles, to predict the toxicity of potential new drugs in medicinal chemistry, to determine the limits of detection for harmful substances in food to predict the toxicity limits of chemicals in the environment, and to predict the exposure limits to harmful substances in the workplace. Despite its importance, a thorough application of toxicity models is still restricted in the field of medicinal chemistry and is virtually overlooked in other scientific domains. Consequently, only a small proportion of the toxicity studies conducted in medicinal chemistry consider the applicability domain in their mathematical models, thereby limiting their predictive power to untested drugs. Conversely, the applicability of these models is crucial; however, this has not been sufficiently assessed in toxicity prediction or in other related areas such as food science, environmental science, and industrial hygiene. Thus, this review sheds light on the prevalent use of Neural Networks in toxicity prediction, thereby serving as a valuable resource for researchers and practitioners across these multifaceted domains that could be extended to other fields in future research