Pricing models for car insurance traditionally use variables related to the policyholder and the insured vehicle (e.g. car brand and driver age) to determine the premium. This can lead to situations where policyholders belonging to a group that is seen as carrying a higher risk for accidents wrongfully get a higher premium, even if the higher risk might not necessarily apply on a per- individual basis. Telematics data offers an opportunity to look at driving behavior during individual trips, enabling a pricing model that can be customized to each policyholder. While these additional variables can be used in a generalized linear model (GLM) similar to the traditional pricing models, machine learning methods can possibly unravel non-linear connections between the variables. Using telematics data, we build a gradient boosting model (GBM) and a neural network (NN) to predict the claim frequency of policyholders on a monthly basis. We find that both GBMs and NNs offer predictive power that can be generalized to data that has not been used in the training of the models. The results of the study also show that telematics data play a considerable role in the model predictions, and that the frequency and distance of trips are important factors in determining the risk using these models. / Prissättningsmodeller för bilförsäkringar använder traditionellt variabler relaterade till försäkringstagaren och det försäkrade fordonet (t.ex. bilmärke och förarålder) för att bestämma försäkringspremien. Detta kan leda till situationer där försäkringstagare som tillhör en grupp som anses bära på en högre risk för olyckor får en felaktigt hög premie, även om den högre risken inte nödvändigtvis gäller på en individbasis. Telematikdata erbjuder en möjlighet att titta på körbeteende under individuella resor, vilket möjliggör en prissättningsmodell som kan anpassas till varje enskild försäkringstagare. Ä ven om dessa variabler kan användas i en linjär modell liknande de traditionella prissättningsmodellerna kan användandet av maskininlärningsmetoder möjligen avslöja icke-linjära samband mellan variablerna. Med hjälp av telematikdata bygger vi en modell baserad på gradient boosting (GBM) och ett neuralt nätverk (NN) för att förutsäga frekvensen av olyckor för försäkringstagare på månadsbasis. Vi kommer fram till att båda modeller har en prediktiv förmåga som går att generalisera till data som inte har använts vid träningen av modellerna. Resultaten av studien visar även att telematikdata spelar en betydande roll i modellernas prediktioner, samt att frekvensen och sträckan av resor är viktiga faktorer vid bedömningen av risken med hjälp av dessa modeller.

京都大学 / 新制・課程博士 / 博士(情報学) / 甲第23397号 / 情博第766号 / 新制||情||131(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 阿久津 達也, 教授 山本 章博, 教授 鹿島 久嗣 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Air pollution and meteorological conditions impact COVID-19 mortality rates. This research studied Quito, Ecuador, using Granger causality tests and regression models to investigate the relationship between pollutants, meteorological variables, human mobility, and excess deaths. Results suggested that Mobility as defined by Google Mobility Index, Facebook Isolation Index, in addition to Nitrogen Dioxide, and Sulphur Dioxide significantly impact excess deaths, while Carbon Monoxide and Relative Humidity have mixed results. Measures to reduce Carbon Monoxide emissions and increase humidity levels may mitigate the impact of air pollution on COVID-19 mortality rates. Further research is needed to investigate the impact of pollutants on COVID-19 transmission in other locations. Healthcare decision-makers must monitor and mitigate the impact of pollutants, promote healthy air quality policies, and encourage physical activity in safe environments. They must also consider meteorological conditions and implement measures such as increased ventilation and air conditioning to reduce exposure. Additionally, they must consider human mobility and reduce it to slow the spread of the diseases. Decisionmakers must monitor and track excess deaths during the pandemic to understand the impact of pollutants, meteorological conditions, and human mobility on human health. Public education is critical to raising awareness of air quality and its impact on health. Encouraging individuals to reduce their exposure to pollutants and meteorological conditions can play a critical role in mitigating the impact of air pollution on respiratory health during the pandemic.

En las últimas cuatro décadas, el método gráfico de estabilidad de Mathews ha constituido el abanico de herramientas indispensables para el dimensionamiento de tajeos; caracterizándose por su eficiencia en costos, ahorro de tiempo y esfuerzo. Asimismo, el aporte de diversos autores por optimizar su rendimiento ha permitido desplegar una serie de criterios que han permitido abordar cada vez más escenarios. No obstante, con la diversificación de la minería en diferentes contextos geológicos y la necesidad trabajar a profundidades más altas se ha mostrado que el método gráfico de estabilidad ha desestimado escenarios con presencia de agua y distintos regímenes de confinamiento. Es por este motivo, que la presente investigación busca incorporar dichos escenarios por medio del algoritmo Gradient Boosting Machine. Para dicho fin, se simuló escenarios con diversos niveles de presión de agua y se consideró el grado de confinamiento alrededor de las excavaciones. El modelo generado se basó en el criterio de la clasificación binaria, siento las clases predichas, “estable” e “inestable”; con lo que se obtuvo un valor AUC de 0.88, lo que demostró una excelente capacidad predictiva del modelo GBM. Asimismo, se demostró las ventajas frente al método tradicional, puesto que se añade una componente de rigurosidad y de generalización. Finalmente, se evidencia el logro de un método de estabilidad que incorpora los esfuerzos activos y que ostenta un adecuado rendimiento predictivo. / In the last four decades, the Mathews' graphical stability method has constituted the range of indispensable tools for the dimensioning of stopes; characterized by its cost efficiency, time and effort savings. Likewise, the contribution of several authors to optimize its performance has made it possible to deploy a series of criteria that have made it possible to address more and more scenarios. However, with the diversification of mining in different geological contexts and the need to work at higher depths, it has been shown that the graphical stability method has neglected scenarios with the presence of water and different confinement regimes. For this reason, the present research sought to incorporate such scenarios by means of the Gradient Boosting Machine algorithm. For this purpose, scenarios with different levels of water pressure were simulated and the degree of confinement around the excavations was considered. The model generated was based on the binary classification criterion, feeling the predicted classes, "stable" and "unstable"; with which an AUC value of 0.88 was obtained, which demonstrated an excellent predictive capacity of the GBM model. Likewise, the advantages over the traditional method were demonstrated since a component of rigor and generalization is added. Finally, the achievement of a stability method that incorporates the active stresses and has an adequate predictive performance is evidenced. / Trabajo de investigación

Artificial neural networks are fascinating machine learning algorithms. They used to be considered unreliable and computationally very expensive. Now it is known that modern neural networks can be quite useful, but their computational expensiveness unfortunately remains. Statistical boosting is considered to be one of the most important machine learning ideas. It is based on an ensemble of weak models that together create a powerful learning system. The goal of this thesis is the comparison of these machine learning models on three use cases. The first use case deals with modeling the probability of burglary in the city of Chicago. The second use case is the typical example of customer churn prediction in telecommunication industry and the last use case is related to the problematic of the computer vision. The second goal of this thesis is to introduce an open-source machine learning platform called H2O. It includes, among other things, an interface for R and it is designed to run in standalone mode or on Hadoop. The thesis also includes the introduction into an open-source software library Apache Hadoop that allows for distributed processing of big data. Concretely into its open-source distribution Hortonworks Data Platform.