Stacked Generalizations in Imbalanced Fraud Data Sets using Resampling Methods

This study uses stacked generalization, which is a two-step process of combining machine learning methods, called meta or super learners, for improving the performance of algorithms in step one (by minimizing the error rate of each individual algorithm to reduce its bias in the learning set) and then in step two inputting the results into the meta learner with its stacked blended output (demonstrating improved performance with the weakest algorithms learning better). The method is essentially an enhanced cross-validation strategy. Although the process uses great computational resources, the resulting performance metrics on resampled fraud data show that increased system cost can be justified. A fundamental key to fraud data is that it is inherently not systematic and, as of yet, the optimal resampling methodology has not been identified. Building a test harness that accounts for all permutations of algorithm sample set pairs demonstrates that the complex, intrinsic data structures are all thoroughly tested. Using a comparative analysis on fraud data that applies stacked generalizations provides useful insight needed to find the optimal mathematical formula to be used for imbalanced fraud data sets.Comment: 19 pages, 3 figures, 8 table

Distributed multi-label learning on Apache Spark

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of individual information measures, and a method that selects the subset of features maximizing the geometric mean. The results indicate that each method excels in different scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets confirm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

A Tale of Two Approaches: Comparing Top-Down and Bottom-Up Strategies for Analyzing and Visualizing High-Dimensional Data

The proliferation of high-throughput and sensory technologies in various fields has led to a considerable increase in data volume, complexity, and diversity. Traditional data storage, analysis, and visualization methods are struggling to keep pace with the growth of modern data sets, necessitating innovative approaches to overcome the challenges of managing, analyzing, and visualizing data across various disciplines. One such approach is utilizing novel storage media, such as deoxyribonucleic acid~(DNA), which presents efficient, stable, compact, and energy-saving storage option. Researchers are exploring the potential use of DNA as a storage medium for long-term storage of significant cultural and scientific materials. In addition to novel storage media, scientists are also focussing on developing new techniques that can integrate multiple data modalities and leverage machine learning algorithms to identify complex relationships and patterns in vast data sets. These newly-developed data management and analysis approaches have the potential to unlock previously unknown insights into various phenomena and to facilitate more effective translation of basic research findings to practical and clinical applications. Addressing these challenges necessitates different problem-solving approaches. Researchers are developing novel tools and techniques that require different viewpoints. Top-down and bottom-up approaches are essential techniques that offer valuable perspectives for managing, analyzing, and visualizing complex high-dimensional multi-modal data sets. This cumulative dissertation explores the challenges associated with handling such data and highlights top-down, bottom-up, and integrated approaches that are being developed to manage, analyze, and visualize this data. The work is conceptualized in two parts, each reflecting the two problem-solving approaches and their uses in published studies. The proposed work showcases the importance of understanding both approaches, the steps of reasoning about the problem within them, and their concretization and application in various domains

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

Cost-sensitive classification based on Bregman divergences

The main object of this PhD. Thesis is the identification, characterization and study of new loss functions to address the so-called cost-sensitive classification. Many decision problems are intrinsically cost-sensitive. However, the dominating preference for cost-insensitive methods in the machine learning literature is a natural consequence of the fact that true costs in real applications are di fficult to evaluate. Since, in general, uncovering the correct class of the data is less costly than any decision error, designing low error decision systems is a reasonable (but suboptimal) approach. For instance, consider the classification of credit applicants as either being good customers (will pay back the credit) or bad customers (will fail to pay o part of the credit). The cost of classifying one risky borrower as good could be much higher than the cost of classifying a potentially good customer as bad. Our proposal relies on Bayes decision theory where the goal is to assign instances to the class with minimum expected cost. The decision is made involving both costs and posterior probabilities of the classes. Obtaining calibrated probability estimates at the classifier output requires a suitable learning machine, a large enough representative data set as well as an adequate loss function to be minimized during learning. The design of the loss function can be aided by the costs: classical decision theory shows that cost matrices de ne class boundaries determined by posterior class probability estimates. Strictly speaking, in order to make optimal decisions, accurate probability estimates are only required near the decision boundaries. It is key to point out that the election of the loss function becomes especially relevant when the prior knowledge about the problem is limited or the available training examples are somehow unsuitable. In those cases, different loss functions lead to dramatically different posterior probabilities estimates. We focus our study on the set of Bregman divergences. These divergences offer a rich family of proper losses that has recently become very popular in the machine learning community [Nock and Nielsen, 2009, Reid and Williamson, 2009a]. The first part of the Thesis deals with the development of a novel parametric family of multiclass Bregman divergences which captures the information in the cost matrix, so that the loss function is adapted to each specific problem. Multiclass costsensitive learning is one of the main challenges in cost-sensitive learning and, through this parametric family, we provide a natural framework to successfully overcome binary tasks. Following this idea, two lines are explored: Cost-sensitive supervised classification: We derive several asymptotic results. The first analysis guarantees that the proposed Bregman divergence has maximum sensitivity to changes at probability vectors near the decision regions. Further analysis shows that the optimization of this Bregman divergence becomes equivalent to minimizing the overall cost regret in non-separable problems, and to maximizing a margin in separable problems. Cost-sensitive semi-supervised classification: When labeled data is scarce but unlabeled data is widely available, semi-supervised learning is an useful tool to make the most of the unlabeled data. We discuss an optimization problem relying on the minimization of our parametric family of Bregman divergences, using both labeled and unlabeled data, based on what is called the Entropy Minimization principle. We propose the rst multiclass cost-sensitive semi-supervised algorithm, under the assumption that inter-class separation is stronger than intra-class separation. The second part of the Thesis deals with the transformation of this parametric family of Bregman divergences into a sequence of Bregman divergences. Work along this line can be further divided into two additional areas: Foundations of sequences of Bregman divergences: We generalize some previous results about the design and characterization of Bregman divergences that are suitable for learning and their relationship with convexity. In addition, we aim to broaden the subset of Bregman divergences that are interesting for cost-sensitive learning. Under very general conditions, we nd sequences of (cost-sensitive) Bregman divergences, whose minimization provides minimum (cost-sensitive) risk for non-separable problems and some type of maximum margin classifiers in separable cases. Learning with example-dependent costs: A strong assumption is widespread through most cost-sensitive learning algorithms: misclassification costs are the same for all examples. In many cases this statement is not true. We claim that using the example-dependent costs directly is more natural and will lead to the production of more accurate classifiers. For these reasons, we consider the extension of cost-sensitive sequences of Bregman losses to example-dependent cost scenarios to generate finely tuned posterior probability estimates

Memory Models for Incremental Learning Architectures

Losing V. Memory Models for Incremental Learning Architectures. Bielefeld: Universität Bielefeld; 2019.Technological advancement leads constantly to an exponential growth of generated data in basically every domain, drastically increasing the burden of data storage and maintenance. Most of the data is instantaneously extracted and available in form of endless streams that contain the most current information. Machine learning methods constitute one fundamental way of processing such data in an automatic way, as they generate models that capture the processes behind the data. They are omnipresent in our everyday life as their applications include personalized advertising, recommendations, fraud detection, surveillance, credit ratings, high-speed trading and smart-home devices. Thereby, batch learning, denoting the offline construction of a static model based on large datasets, is the predominant scheme. However, it is increasingly unfit to deal with the accumulating masses of data in given time and in particularly its static nature cannot handle changing patterns. In contrast, incremental learning constitutes one attractive alternative that is a very natural fit for the current demands. Its dynamic adaptation allows continuous processing of data streams, without the necessity to store all data from the past, and results in always up-to-date models, even able to perform in non-stationary environments. In this thesis, we will tackle crucial research questions in the domain of incremental learning by contributing new algorithms or significantly extending existing ones. Thereby, we consider stationary and non-stationary environments and present multiple real-world applications that showcase merits of the methods as well as their versatility. The main contributions are the following: One novel approach that addresses the question of how to extend a model for prototype-based algorithms based on cost minimization. We propose local split-time prediction for incremental decision trees to mitigate the trade-off between adaptation speed versus model complexity and run time. An extensive survey of the strengths and weaknesses of state-of-the-art methods that provides guidance for choosing a suitable algorithm for a given task. One new approach to extract valuable information about the type of change in a dataset. We contribute a biologically inspired architecture, able to handle different types of drift using dedicated memories that are kept consistent. Application of the novel methods within three diverse real-world tasks, highlighting their robustness and versatility. Investigation of personalized online models in the context of two real-world applications