Cross-validated Tree-Based Models for Multi-target Learning

Yehuda Nissenbaum, M.Sc student  Advisor:  Dr. Amichai Painsky 

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Abstract:
Multi-target learning (MTL) is a popular machine learning technique which considers simultaneous prediction of multiple targets. The most common MTL schemes utilize traditional linear models and more contemporary deep neural networks. Interestingly, tree-based MTL methods received limited attention over the years. In this work, we introduce a novel tree-based MTL scheme which exploits the correlation between the targets to obtain improved prediction accuracy. Our suggested scheme applies cross-validated splitting criteria to identify correlated targets at every node of the tree. This allows us to benefit from the correlation among the targets while avoiding overfitting. We demonstrate the performance of our proposed scheme in a variety of synthetic and real-world experiments, showing a significant improvement over alternative methods

Bio:

Yehuda Nissenbaum, M.Sc. student (fast track program) at the Department of Industrial Engineering and management at Tel Aviv University, specializing in business intelligence and data science. Yehuda holds a B.Sc. degree in Industrial Engineering from Tel Aviv University. His research is supervised by Dr. Amichai Painsky.

Contact:

• E-Mail: yehudanis169@gmail.com

• LinkedIn: https://www.linkedin.com/in/yehuda-nissenbaum/