Filter Methods for Feature Selection in Supervised Machine Learning Applications : Review and Benchmark

The amount of data for machine learning (ML) applications is constantly growing. Not only the number of observations, especially the number of measured variables (features) increases with ongoing digitization. Selecting the most appropriate features for predictive modeling is an important lever for the success of ML applications in business and research. Feature selection methods (FSM) that are independent of a certain ML algorithm — so-called filter methods — have been numerously suggested, but little guidance for researchers and quantitative modelers exists to choose appropriate approaches for typical ML problems. This review synthesizes the substantial literature on feature selection benchmarking and evaluates the performance of 58 methods in the widely used R environment. For concrete guidance, we consider four typical dataset scenarios that are challenging for ML models (noisy, redundant, imbalanced data and cases with more features than observations). Drawing on the experience of earlier benchmarks, which have considered much fewer FSMs, we compare the performance of the methods according to four criteria (predictive performance, number of relevant features selected, stability of the feature sets and runtime). We found methods relying on the random forest approach, the double input symmetrical relevance filter (DISR) and the joint impurity filter (JIM) were well-performing candidate methods for the given dataset scenarios

ifb/Familienreport : Familien in der Corona-Pandemie ; Unterstützung durch Familienbildung und Beratung in Bayern

Die Corona-Pandemie hat den Alltag der Menschen stark verändert. Für Familien waren neben allgemeinen Kontaktbeschränkungen besonders die phasenweisen (Teil-) Schließungen der Bildungs- und Betreuungseinrichtungen eine große Herausforderung. Dieser Familienreport beleuchtet aus dem Blickwinkel der Fachkräfte, die in den Einrichtungen der Familienbildung und Beratung tätig sind, welche Unterstützungsbedarfe Familien in Bayern im Frühjahr 2021 nach einem Jahr Pandemie hatten und auf welche Weise Familienbildung und mit Eltern und Schwangeren in dieser herausfordernden Zeit erreichbar und in Kontakt geblieben sind. Er basiert auf Daten, die im Rahmen des Projekts kontakt.los! vom ifb / Staatsinstitut für Familienforschung an der Universität Bamberg erhoben wurden. Die Ergebnisse zeigen, dass im Mai 2021 noch immer weniger primärpräventive Angebote gemacht wurden als vor der Pandemie und auch insgesamt die Teilnahmezahlen noch geringer waren. Gleichzeitig zeigt sich auch, dass die Situation für die Familien sehr belastend war: Ratsuchende wandten sich vermehrt mit problembezogenen Anliegen an die Einrichtungen und die Fachkräfte nahmen mehr Fälle von Kindeswohlgefährdungen wahr. Die durch die Pandemie geänderten Rahmenbedingungen waren auch selbst Gegenstand der Anliegen der Ratsuchenden: Fragen rund um institutionelle Kinderbetreuung und Schule haben zugenommen, so wie auch Fragen zum Umgang mit Medien. Anliegen im Bereich der Primärprävention, insbesondere zur kindlichen Entwicklung und zu Aufklärung, Familienplanung und Verhütung sind hingegen stark in den Hintergrund getreten. Die Fachkräfte der Familienbildung und Beratung haben in dem einen Jahr zwischen Pandemiebeginn und dem Frühjahr 2021 bereits zahlreiche Angebote angepasst, um den veränderten Bedingungen und Bedarfen zu entsprechen. Einige Formate, die in der Vergangenheit eine wichtige Rolle gespielt haben, konnten nicht fortgeführt werden, so dass dafür analoge und digitale Alternativen gefunden werden mussten. Beratungsgespräche konnten gut an die Pandemiebedingungen angepasst werden, Gruppenangebote, wie Offene Treffs, Eltern-Cafés und Eltern-Kind-Gruppen ließen sich hingegen weniger gut an anpassen. Durch die notwendigen Anpassungen sind Familienbildung und Beratung in der Digitalisierung weit vorangeschritten. Das kann auch nach der Pandemie hilfreich sein, da Eltern und Schwangere in einigen Situationen mit digitalisierten Angeboten leichter erreicht werden können als mit analogen

On the Relationship between Telework and Health in Germany : Causal or Selection Effects?

Teleworking has become a popular work arrangement in many developed countries. Although there are heated public debates over the benefits of teleworking, empirical evidence on the causal relationship between teleworking and health is still rare. Using panel data from the German BAuA Working Time Survey (2015, 2017, and 2019), the authors investigated the effects of teleworking on health and well-being. The authors applied an innovative research design to underscore different sources of selection. Overall, no concrete evidence was found for the positive effect of teleworking on workers’ self-reported health, quality of sleep, and psychosomatic conditions. The ostensible better health outcomes among teleworkers could be partially explained by the positive selection on both prior levels and prior trajectories of health into teleworking. Moreover, the health impacts of telework were contingent on workers’ gender and parenthood status and the intensity of teleworking. These findings indicate that the positive association between teleworking and health appears to reflect selection bias rather than a causal relationship in Germany before the COVID-19 pandemic

International adjustment to an oil price shock : the role of competitiveness

It is common political practice to blame the presently poor performance of OECD economies on huge raw material price increases during the 70s. This view is apparently backed by recent small-country oil-shock analysis showing within theoretical models how trade deficits and output and employment losses inevitably occur in net oil importing countries. This paper argues that, by its very nature, an oil shock afflicts an OECD country not in isolation. This requires theoretical analysis of an oil-importing economy together with its major trading partners. The paper demonstrates that due to a country-specific superior technological adjustment the oil shock may possibly give a competitive edge to one country or a group of OECD countries. Then a trade diversion among trading OECD economies benefits a few of them at the expense of others and may be strong enough to weaken or even turn around negative output and employment effects which originated from the real income transfer towards oil producers. Additionally, assessment of real and price level effects of an exchange rate change reveals that in general the beggar-my-neighbor property of a devaluation can be destroyed if imported intermediate goods like oil are taken into account. This outcome should also have some bearing on economic modelling of transatlantic relations under flexible exchange rates

Overcoming Anchoring Bias : the Potential of AI and XAI-based Decision Support

Information systems (IS) are frequently designed to leverage the negative effect of anchoring bias to influence individuals’ decision-making (e.g., by manipulating purchase decisions). Recent advances in Artificial Intelligence (AI) and the explanations of its decisions through explainable AI (XAI) have opened new opportunities for mitigating biased decisions. So far, the potential of these technological advances to overcome anchoring bias remains widely unclear. To this end, we conducted two online experiments with a total of N=390 participants in the context of purchase decisions to examine the impact of AI and XAI-based decision support on anchoring bias. Our results show that AI alone and its combination with XAI help to mitigate the negative effect of anchoring bias. Ultimately, our findings have implications for the design of AI and XAI-based decision support and IS to overcome cognitive biases