New Patterns of Management.

In this paper, we study the performance of various state-of-the-art classification algorithms applied to eight real-life credit scoring data sets. Some of the data sets originate from major Benelux and UK financial institutions. Different types of classifiers are evaluated and compared. Besides the well-known classification algorithms (eg logistic regression, discriminant analysis, k-nearest neighbour, neural networks and decision trees), this study also investigates the suitability and performance of some recently proposed, advanced kernel-based classification algorithms such as support vector machines and least-squares support vector machines (LS-SVMs). The performance is assessed using the classification accuracy and the area under the receiver operating characteristic curve. Statistically significant performance differences are identified using the appropriate test statistics. It is found that both the LS-SVM and neural network classifiers yield a very good performance, but also simple classifiers such as logistic regression and linear discriminant analysis perform very well for credit scoring.

Benchmarking state-of-the-art classification algorithms for credit scoring

https://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1038&context=cmsc_pubs

Ensemble learning for data stream analysis

Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

The prevalence of mobile phones, the internet-of-things technology, and networks of sensors has led to an enormous and ever increasing amount of data that are now more commonly available in a streaming fashion [1]-[5]. Often, it is assumed - either implicitly or explicitly - that the process generating such a stream of data is stationary, that is, the data are drawn from a fixed, albeit unknown probability distribution. In many real-world scenarios, however, such an assumption is simply not true, and the underlying process generating the data stream is characterized by an intrinsic nonstationary (or evolving or drifting) phenomenon. The nonstationarity can be due, for example, to seasonality or periodicity effects, changes in the users' habits or preferences, hardware or software faults affecting a cyber-physical system, thermal drifts or aging effects in sensors. In such nonstationary environments, where the probabilistic properties of the data change over time, a non-adaptive model trained under the false stationarity assumption is bound to become obsolete in time, and perform sub-optimally at best, or fail catastrophically at worst.

/pdf/learning-in-nonstationary-environments-a-survey-4qv18ybr8l.pdf

Learning in Nonstationary Environments: A Survey

Equipment and process for three-dimensional measurement of size and shape and for compositional analysis of mineral and rock particles and the like objects. A mixture of particles or objects of same or different sizes of minerals or rocks or the like are fed individually and automatically onto a conveyor belt for three-dimensional machine vision measurements using laser and two cameras and subsequently for spectroscopic measurements using visible and infrared light and are then collected at the end of the conveyor. Computer software is used to perform the measurement automatically and to calculate size, form, roundness, and preferably petrographic composition and other characteristics or properties of each individual object and the statistical distribution of relevant properties, either according to built-in measurement processes or user specific methods.

Apparatus and method for analysis of size, form and angularity and for compositional analysis of mineral and rock particles

Changes in underlying distributions over time are a challenging problem in supervised learning. While this problem of drift is subject to an increasing effort in research, some definitions required for proper distinction of types of drift remain ambiguous. Furthermore, the approaches discussed in literature so far require new, labelled data for incremental model updates. However, there are domains in which such data is scarce or only available with a considerable time lag, a so-called verification latency. This issues are addressed in this paper: First, the different notations used in literature are related, and an overview over types of drift is given. Second, following the change mining paradigm, explicit models of drift are introduced. These drift models can be employed when actual, labelled data is scarce or not available at all, as they allow to anticipate changes in distributions over time. Third, an exemplary drift-adaptive learning strategy that employs such a drift model is presented: Using an expectation-maximisation algorithm, a mixture of subpopulations is tracked. As a result, the classification model can be updated using solely new, unlabelled data.

http://wwwis.win.tue.nl/hacdais2011/slides_Krempl.pdf

Classification in Presence of Drift and Latency

The increasing automation in many areas of the Industry expressly demands to design efficient machine learning solutions for the detection of abnormal events. With the ubiquitous deployment of sensors monitoring nearly continuously the health of complex infrastructures, anomaly detection can now rely on measurements sampled at a very high frequency, providing a very rich representation of the phenomenon under surveillance. In order to exploit fully the information thus collected, the observations cannot be treated as multivariate data anymore and a functional analysis approach is required. It is the purpose of this paper to investigate the performance of recent techniques for anomaly detection in the functional setup on real datasets. After an overview of the state of the art and a visual-descriptive study, a variety of anomaly detection methods are compared. While taxonomies of abnormalities (e.g., shape, location) in the functional setup are documented in the literature, assigning a specific type to the identified anomalies appears to be a challenging task. Thus, strengths and weaknesses of the existing approaches are benchmarked in view of these highlighted types in a simulation study. Anomaly detection methods are next evaluated on two datasets, related to the monitoring of helicopters in flight and to the spectrometry of construction materials namely. The benchmark analysis is concluded by a recommendation guidance for practitioners. 

/pdf/functional-anomaly-detection-a-benchmark-study-3mfd0jjy.pdf

Functional anomaly detection: a benchmark study

The Los Angeles test is one of the few mechanical test methods that provides information on the quality of railway ballast. However, the Los Angeles value is subject to large variability. Since important economic decisions depend on this value, the reasons for its variability are investigated. An extensive series of tests using four types of rock as well as an in-depth analysis of particle geometry and petrography are carried out. The impact of particle characteristics on the test results is investigated. The deviation of the petrographic composition within a given sample turns out to have a considerable impact on the Los Angeles test results, whereas the influence of the respective deviation of particle geometry is relatively small. The latter effect only comes into play in connection with petrographically homogeneous rock types. The distribution of the geometric features is similar in almost all of the rock types investigated. Due to the large deviation in particle shape and angularity, the sample mass of 10 kg (as provided in the standards EN 1097-2 and EN 13450) is not found to be representative. The necessary number of test repetitions in order to exclude the effect of deviation of particle geometry is estimated. The one result parameter according to the standard, the Los Angeles value, does not allow for discriminating between the amount of abrasion and the fragmentation occurring during the test. An additional result parameter for the estimation of the fragmentation rate is therefore proposed.

Impact of Geometric and Petrographic Characteristics on the Variability of LA Test Values for Railway Ballast

We present a bivariate regression model for count data that allows for positive as well as negative correlation of the response variables. The covariance structure is based on the Sarmanov distribution and consists of a product of generalised Poisson marginals and a factor that depends on particular functions of the response variables. The closed form of the probability function is derived by means of the moment-generating function. The model is applied to a large real dataset on health care demand. Its performance is compared with alternative models presented in the literature. We find that our model is significantly better than or at least equivalent to the benchmark models. It gives insights into influences on the variance of the response variables.

A bivariate Sarmanov regression model for count data with generalised Poisson marginals

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