ML Anthology

A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns

Michèle Sebag, Nicolas Tarrisson, Olivier Teytaud, Julien Lefèvre, Sylvain Baillet
IJCAI 2005 pp. 859-864
/ijcai/2005/sebag2005ijcai-multi/

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. 1

PDF Semantic Scholar

Cite

Text

Sebag et al. "A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns." International Joint Conference on Artificial Intelligence, 2005.

Markdown

[Sebag et al. "A Multi-Objective Multi-Modal Optimization Approach for Mining Stable Spatio-Temporal Patterns." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/sebag2005ijcai-multi/)

BibTeX

@inproceedings{sebag2005ijcai-multi,
  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 = {International Joint Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {859-864},
  url       = {https://mlanthology.org/ijcai/2005/sebag2005ijcai-multi/}
}