A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 859-863.
Abstract
This paper, motivated by functional brain imaging applications, is interested in the discovery of stable spatio-temporal patterns. This problem is formalized as a multi-objective multi-modal optimization problem: on one hand, the target patterns must show a good stability in a wide spatio-temporal region (antagonistic objectives); on the other hand, experts are interested in finding all such patterns (global and local optima). The proposed algorithm, termed 4D-Miner, is empirically validated on artificial and real-world datasets; it shows good performances and scalability, detecting target spatiotemporal patterns within minutes from 400+ Mo datasets.
@inproceedings{ijcai2005p127,
title = {A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns},
author = {Sebag, Michèle and Tarrisson, Nicolas and Teytaud, Olivier and Lefèvre, Julien and Baillet, Sylvain},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {859--863},
year = {2005},
url = {https://ijcai.org/proceedings/2005/127},
}
TY - CPAPER TI - A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns AU - Sebag, Michèle AU - Tarrisson, Nicolas AU - Teytaud, Olivier AU - Lefèvre, Julien AU - Baillet, Sylvain T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 859 EP - 863 UR - https://ijcai.org/proceedings/2005/127 ER -Copied
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