Description : This package includes the Python code of the HAL-EMO framework [1] for human assisted learning. HAL-EMO minimizes the number of selected instances for human decision and the total errors simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve it. Experiments show the superiority of HAL-EMO over previous algorithms, and README files are included in the package, showing how to use the code.

References: [1] Dan-Xuan Liu, Xin Mu, and Chao Qian. Human Assisted Learning by Evolutionary Multi-objective Optimization. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington, DC, 2023, pp.12453-12461.

ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Dr. Chao Qian (qianc@lamda.nju.edu.cn).

Requirement: The package was developed with Python.

ATTN2: This package was developed by Ms. Dan-Xuan Liu (liudx@lamda.nju.edu.cn). For any problem concerning the code, please feel free to contact Ms. Liu.

Download: code (2.97MB)