معرفی
Jurica Levatić is a researcher at the Jožef Stefan Institute and the Jožef Stefan International Postgraduate School in Ljubljana, Slovenia. He is actively involved in research in machine learning and data mining, particularly in the development of semi-supervised learning methods for complex prediction tasks.
His research focuses on advancing predictive modeling through interpretable tree-based methods such as Predictive Clustering Trees (PCTs), with applications in multi-label and hierarchical multi-label classification. His work integrates unlabeled data to improve model performance while maintaining interpretability, a critical aspect in intelligent systems. He also explores ensemble methods and feature weighting to enhance predictive accuracy.
His recent publication in the International Journal of Intelligent Systems demonstrates strong contributions to semi-supervised learning frameworks, with extensive experimental validation across diverse domains including text, audio, biology, and ecology. The research shows significant performance gains over supervised baselines, particularly in low-label regimes.
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Labs and Teams: Jurica Levatić is affiliated with the Jožef Stefan Institute, a leading research institution in Slovenia, where he collaborates with experts like Sašo Džeroski and Dragi Kocev in the field of inductive machine learning and intelligent systems.
