Jennifer Landes

My research focuses on the design, enhancement, and evaluation of a preprocessing pipeline for eye-tracking data. The pipeline consists of multiple stages, including the detection and treatment of missing values and outliers, data normalization, as well as optional steps such as, smoothing and filtering. The pipeline's evaluation involves applying various methods at each stage and analyzing their impact on classification performance, specifically using a Random Forest classifier.

Research Interests

• Mathematical models of Maschine Learning
• Data Science
• Artificial Intelligence
• Data Preprocessing

Publications

• Jennifer Landes, Meike Klettke, Sonja K?ppl: Impact of Preprocessing on Classification Results of Eye-Tracking-Data. DE4DS@BTW 2025 (accepted)

• Dominique Hausler, Jennifer Landes, Meike Klettke: SeeME: A General, Reusable Graph Schema for Data Preprocessing of Eye-Tracking Data. DE4DS@BTW 2025 (accepted)

• Jennifer Landes, Meike Klettke, Sonja K?ppl: Comparison of Classifiers for Eye-Tracking Data, Data Science Projekte: Von der Wissenschaft bis zur Anwendung@GI Informatik Festival 2024

• Jennifer Landes, Sonja K?ppl, Meike Klettke: Data Preprocessing Pipeline for Eye-Tracking Analysis. GvDB 2024

• Jennifer Landes, Sonja K?ppl, Meike Klettke: Influence Factors on Academic Integrity revealed by Machine Learning Methods. GvDB 2023