Talks

I regularly give talks on machine learning topics at academic conferences, data science meetups and as part of my teaching activities at HU Berlin. Below you can find links to videos and slides of selected talks.

I regularly give talks on machine learning topics at academic conferences, data science meetups and as part of my teaching activities at HU Berlin. Below you can find links to videos and slides of selected talks.

Conferences

  • Multi-Objective Particle Swarm Optimization for Feature Selection in Credit Scoring
    Workshop at European Conference on Machine Learning and PKDD, 2020 (Ghent)
  • Abstract: Credit scoring refers to the use of statistical models to support loan approval decisions. An ever-increasing availability of data on potential borrowers emphasizes the importance of feature selection for scoring models. Traditionally, feature selection has been viewed as a single-objective task. Recent research demonstrates the effectiveness of multi-objective approaches. We propose a novel multi-objective feature selection framework for credit scoring that extends previous work by taking into account data acquisition costs and employing a state-of-the-art particle swarm optimization algorithm. Our framework optimizes three fitness functions: the number of features, data acquisition costs and the AUC. Experiments on nine credit scoring data sets demonstrate a highly competitive performance of the proposed framework.

  • Shallow Self-Learning for Reject Inference in Credit Scoring
    European Conference on Machine Learning and PKDD, 2019 (Würzburg)
  • Abstract: Credit scoring models support loan approval decisions in the financial services industry. Lenders train these models on data from previously granted credit applications, where the borrowers’ repayment behavior has been observed. This approach creates sample bias. The scoring model is trained on accepted cases only. Applying the model to screen applications from the population of all borrowers degrades its performance. Reject inference comprises techniques to overcome sampling bias through assigning labels to rejected cases. This paper makes two contributions. First, we propose a self-learning framework for reject inference. The framework is geared toward real-world credit scoring requirements through considering distinct training regimes for labeling and model training. Second, we introduce a new measure to assess the effectiveness of reject inference strategies. Our measure leverages domain knowledge to avoid artificial labeling of rejected cases during evaluation. We demonstrate this approach to offer a robust and operational assessment of reject inference. Experiments on a real-world credit scoring data set confirm the superiority of the suggested self-learning framework over previous reject inference strategies. We also find strong evidence in favor of the proposed evaluation measure assessing reject inference strategies more reliably, raising the performance of the eventual scoring model.

  • Profit-Oriented Feature Selection in Credit Scoring Applications
    Conference on Operations Research, 2018 (Brussels)
  • Abstract: In credit scoring, feature selection aims at removing irrelevant data to improve the performance of the scorecard and its interpretability. Standard feature selection techniques are based on statistical criteria such as correlation. Recent studies suggest that using profit-based indicators for model evaluation may improve the quality of scoring models for businesses. We extend the use of profit measures to feature selection and develop a wrapper-based framework that uses the Expected Maximum Profit measure (EMP) as a fitness function. Experiments on multiple credit scoring data sets provide evidence that EMP-maximizing feature selection helps to develop scorecards that yield a higher expected profit compared to conventional feature selection strategies.



Meetups

Selected talks will be added soon.