1,720,954 research outputs found
Tröskelbaserad strategioptimering med tolkbara modeller för kreditriskbedömning
No full textRental guarantees are a relatively new concept in the Swedish second-hand rental market, which offers an alternative to traditional cash deposits, such that landlords are insured against tenants' defaulting. While this product improves accessibility for tenants and safety for the landlords, it introduces new challenges in risk assessment and strategy design, which remain unexplored in current research. This thesis addresses a data-driven approach to improve decision-making by predicting tenant default risk and optimising deposit strategies. The risk is predicted using a combination of machine learning models: XGBoost, Neural Networks (NN), and Support Vector Machines (SVM), and converted into strategies aimed at maximising the expected profit. The XGBoost models outperformed the NN models in terms of AUC-PR, reaching a maximum score of 0.4571. By using SHAP values, the model's interpretability was ensured, revealing that 13.4% of the importance comes from the currently used features, indicating a more complete view of the risk. Although ensemble methods slightly outperformed individual models, the individual XGBoost model is recommended due to its simplicity and comparable performance. Despite limitations in data quality and assumptions regarding the input parameters, the model leads to a profit increase of 6.4% when allowing for one-month deposits, which is reached by reducing severity and having a better deposit distribution compared to the current strategy. Ethical considerations were addressed by excluding sensitive features, with minimal impact on the model's performance.Future work includes validating the model in production and expanding to different markets. The results offer a scalable, interpretable, and ethically grounded framework for optimising rental guarantee decisions.Hyresgarantier är ett relativt nytt fenomen på den svenska andrahandsmarknaden för hyresrätter. De fungerar som ett alternativ till traditionella kontantdepositioner och erbjuder ett skydd för hyresvärdar vid uteblivna betalningar. Samtidigt som hyresgarantier förbättrar tillgängligheten för hyresgäster och tryggheten för hyresvärdar, innebär det nya utmaningar i riskbedömning och strategiutformning, områden som varit outforskade i existerande forskning. Detta examensarbete använder ett datadrivet angreppssätt för att förbättra beslutsfattandet genom att förutsäga risken att hyresgästens betalning uteblir, och optimera depositionsstrategier. Riskprognoserna görs med hjälp av olika maskininlärningsmodeller: XGBoost, neurala nätverk (NN) och Support Vector Machines (SVM), och omvandlas sedan till strategier som syftar till att maximera den förväntade vinsten. XGBoost modellerna presterade bättre än NN modellerna utifrån AUC-PR, med ett högsta AUC-PR-värde på 0.4571. För att säkerställa tolkbarhet användes SHAP-värden, vilket visade att 13.4% av modellens vikt kommer från de datapunkter som använda i den existerade modellen, vilket ger en mer fullständig bild av risken. Trots att ensembles av modeller presterade något bättre, rekommenderas den enskilda XGBoost-modellen tack vare sin enkelhet och jämförbara prestanda. Trots begränsningar i datakvalitet och antaganden gjorda för inmatningsparametrar, möjliggör modellen en potentiell vinstökning på 6.4% när en månads deposition tillåts, genom att minska ekonomisk förlust och förbättra depositions distributionen jämfört med nuvarande strategi. Etisk hänsyn har tagits genom att exkludera känsliga variabler, vilket visade sig ha minimal påverkan på resultatet. Framtida arbete bör fokusera på att validera modellen i produktionsmiljö samt undersöka möjligheter att tillämpa metoden på andra marknader. Resultatet visar på ett skalbart, transparent och etiskt förankrat ramverk för att optimera beslutsfattande kring hyresgarantier
Tröskelbaserad strategioptimering med tolkbara modeller för kreditriskbedömning
No full textRental guarantees are a relatively new concept in the Swedish second-hand rental market, which offers an alternative to traditional cash deposits, such that landlords are insured against tenants' defaulting. While this product improves accessibility for tenants and safety for the landlords, it introduces new challenges in risk assessment and strategy design, which remain unexplored in current research. This thesis addresses a data-driven approach to improve decision-making by predicting tenant default risk and optimising deposit strategies. The risk is predicted using a combination of machine learning models: XGBoost, Neural Networks (NN), and Support Vector Machines (SVM), and converted into strategies aimed at maximising the expected profit. The XGBoost models outperformed the NN models in terms of AUC-PR, reaching a maximum score of 0.4571. By using SHAP values, the model's interpretability was ensured, revealing that 13.4% of the importance comes from the currently used features, indicating a more complete view of the risk. Although ensemble methods slightly outperformed individual models, the individual XGBoost model is recommended due to its simplicity and comparable performance. Despite limitations in data quality and assumptions regarding the input parameters, the model leads to a profit increase of 6.4% when allowing for one-month deposits, which is reached by reducing severity and having a better deposit distribution compared to the current strategy. Ethical considerations were addressed by excluding sensitive features, with minimal impact on the model's performance.Future work includes validating the model in production and expanding to different markets. The results offer a scalable, interpretable, and ethically grounded framework for optimising rental guarantee decisions.Hyresgarantier är ett relativt nytt fenomen på den svenska andrahandsmarknaden för hyresrätter. De fungerar som ett alternativ till traditionella kontantdepositioner och erbjuder ett skydd för hyresvärdar vid uteblivna betalningar. Samtidigt som hyresgarantier förbättrar tillgängligheten för hyresgäster och tryggheten för hyresvärdar, innebär det nya utmaningar i riskbedömning och strategiutformning, områden som varit outforskade i existerande forskning. Detta examensarbete använder ett datadrivet angreppssätt för att förbättra beslutsfattandet genom att förutsäga risken att hyresgästens betalning uteblir, och optimera depositionsstrategier. Riskprognoserna görs med hjälp av olika maskininlärningsmodeller: XGBoost, neurala nätverk (NN) och Support Vector Machines (SVM), och omvandlas sedan till strategier som syftar till att maximera den förväntade vinsten. XGBoost modellerna presterade bättre än NN modellerna utifrån AUC-PR, med ett högsta AUC-PR-värde på 0.4571. För att säkerställa tolkbarhet användes SHAP-värden, vilket visade att 13.4% av modellens vikt kommer från de datapunkter som använda i den existerade modellen, vilket ger en mer fullständig bild av risken. Trots att ensembles av modeller presterade något bättre, rekommenderas den enskilda XGBoost-modellen tack vare sin enkelhet och jämförbara prestanda. Trots begränsningar i datakvalitet och antaganden gjorda för inmatningsparametrar, möjliggör modellen en potentiell vinstökning på 6.4% när en månads deposition tillåts, genom att minska ekonomisk förlust och förbättra depositions distributionen jämfört med nuvarande strategi. Etisk hänsyn har tagits genom att exkludera känsliga variabler, vilket visade sig ha minimal påverkan på resultatet. Framtida arbete bör fokusera på att validera modellen i produktionsmiljö samt undersöka möjligheter att tillämpa metoden på andra marknader. Resultatet visar på ett skalbart, transparent och etiskt förankrat ramverk för att optimera beslutsfattande kring hyresgarantier
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
