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Found 16 papers, 4 papers with code
Inferring the relationship between soil temperature and the normalized difference vegetation index with machine learning
Ultimately, this study contributes to our knowledge of the relationships between soil temperature, meteorological variables, and vegetation phenology, providing valuable insights for predicting vegetation phenology characteristics and managing subarctic grasslands in the face of climate change.
Tree-based Forecasting of Day-ahead Solar Power Generation from Granular Meteorological Features
Accurate forecasts for day-ahead photovoltaic (PV) power generation are crucial to support a high PV penetration rate in the local electricity grid and to assure stability in the grid.
A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions
In lending, where prices are specific to both customers and products, having a well-functioning personalized pricing policy in place is essential to effective business making.
Learning continuous-valued treatment effects through representation balancing
In this work, we propose CBRNet, a causal machine learning approach to estimate an individual dose response from observational data.
TSLiNGAM: DirectLiNGAM under heavy tails
TSLiNGAM leverages the non-Gaussianity assumption of the error terms in the LiNGAM model to obtain more efficient and robust estimation of the causal structure.
Fast Linear Model Trees by PILOT
Linear model trees are regression trees that incorporate linear models in the leaf nodes.
On the causality-preservation capabilities of generative modelling
Breakthroughs in these fields often come with the requirement of large amounts of data.
Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field
In addition, we build a framework for fraud analytical methods and propose a keywording strategy for future research.
Prescriptive maintenance with causal machine learning
This work proposes to relax both assumptions by learning the effect of maintenance conditional on a machine's characteristics from observational data on similar machines using existing methodologies for causal inference.
A new perspective on classification: optimally allocating limited resources to uncertain tasks
A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain.
Computational Efficient Approximations of the Concordance Probability in a Big Data Setting
Contrary to AUC, the concordance probability can also be extended to the situation with a continuous response variable.
Weight-of-evidence 2.0 with shrinkage and spline-binning
We present the results of a series of experiments in a fraud detection setting, which illustrate the effectiveness of the presented approach.
Instance-Dependent Cost-Sensitive Learning for Detecting Transfer Fraud
Fraud detection is to be acknowledged as an instance-dependent cost-sensitive classification problem, where the costs due to misclassification vary between instances, and requiring adapted approaches for learning a classification model.
Applications
robROSE: A robust approach for dealing with imbalanced data in fraud detection
Detecting fraud in such a highly imbalanced data set typically leads to predictions that favor the majority group, causing fraud to remain undetected.
Concordance probability in a big data setting: application in non-life insurance
Due to the typical large sample size of the frequency data in particular, two different adaptations of the estimation procedure of the concordance probability are presented.
Profit Driven Decision Trees for Churn Prediction
Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base.
