Classifier Calibration: A survey on how to assess and improve predicted class probabilities

This paper provides both an introduction to and a detailed overview of the principles and practice of classifier calibration. A well-calibrated classifier correctly quantifies the level of uncertainty or confidence associated with its instance-wise predictions. This is essential for critical applications, optimal decision making, cost-sensitive classification, and for some types of context change. Calibration research has a rich history which predates the birth of machine learning as an academic field by decades. However, a recent increase in the interest on calibration has led to new methods and the extension from binary to the multiclass setting. The space of options and issues to consider is large, and navigating it requires the right set of concepts and tools. We provide both introductory material and up-to-date technical details of the main concepts and methods, including proper scoring rules and other evaluation metrics, visualisation approaches, a comprehensive account of post-hoc calibration methods for binary and multiclass classification, and several advanced topics

Statistical Data Modeling and Machine Learning with Applications

The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section “Mathematics and Computer Science”. Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties

Identifying Structure Transitions Using Machine Learning Methods

Methodologies from data science and machine learning, both new and old, provide an exciting opportunity to investigate physical systems using extremely expressive statistical modeling techniques. Physical transitions are of particular interest, as they are accompanied by pattern changes in the configurations of the systems. Detecting and characterizing pattern changes in data happens to be a particular strength of statistical modeling in data science, especially with the highly expressive and flexible neural network models that have become increasingly computationally accessible in recent years through performance improvements in both hardware and algorithmic implementations. Conceptually, the machine learning approach can be regarded as one that employing algorithms that eschew explicit instructions in favor of strategies based around pattern extraction and inference driven by statistical analysis and large complex data sets. This allows for the investigation of physical systems using only raw configurational information to make inferences instead of relying on physical information obtained from a priori knowledge of the system. This work focuses on the extraction of useful compressed representations of physical configurations from systems of interest to automate phase classification tasks in addition to the identification of critical points and crossover regions

Adaptive prototype-based dissimilarity learning

Zhu X. Adaptive prototype-based dissimilarity learning. Bielefeld: Universitätsbibliothek Bielefeld; 2015.In this thesis we focus on prototype-based learning techniques, namely three unsuper- vised techniques: generative topographic mapping (GTM), neural gas (NG) and affinity propagation (AP), and two supervised techniques: generalized learning vector quantiza- tion (GLVQ) and robust soft learning vector quantization (RSLVQ). We extend their abilities with respect to the following central aspects: • Applicability on dissimilarity data: Due to the increased complexity of data, in many cases data are only available in form of (dis)similarities which describe the relations between objects. Classical methods can not directly deal with this kind of data. For unsupervised methods this problem has been studied, here we transfer the same idea to the two supervised prototype-based techniques such that they can directly deal with dissimilarities without an explicit embedding into a vector space. • Quadratic complexity issue: For dealing with dissimilarity data, due to the need of the full dissimilarity matrix, the complexity becomes quadratic which is infeasible for large data sets. In this thesis we investigate two linear approximation techniques: Nyström approximation and patch processing, and integrate them into unsupervised and supervised prototype-based techniques. • Reliability of prototype-based classifiers: In practical applications, a relia- bility measure is beneficial for evaluating the classification quality expected by the end users. Here we adopt concepts from conformal prediction (CP), which provides point-wise confidence measure of the prediction, and we combine those with supervised prototype-based techniques. • Model complexity: By means of the confidence values provided by CP, the model complexity can be automatically adjusted by adding new prototypes to cover low confidence data space. • Extendability to semi-supervised problems: Besides its ability to evaluate a classifier, conformal prediction can also be considered as a classifier. This opens a way that supervised techniques can be easily extended for semi-supervised settings by means of a self-training approach

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported