Learning Qualitative Models from Numerical Data: Extended abstract
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3195-3199.
Abstract
Qualitative models are predictive models that describe how changes in values of input variables affect the output variable in qualitative terms, e.g. increasing or decreasing. We describe Padé, a new method for qualitative learning which estimates partial derivatives of the target function from training data and uses them to induce qualitative models of the target function. We formulated three methods for computation of derivatives, all based on using linear regression on local neighbourhoods. The methods were empirically tested on artificial and real-world data. We also provide a case study which shows how the developed methods can be used in practice.
@inproceedings{ijcai2013p485,
title = {Learning Qualitative Models from Numerical Data: Extended abstract},
author = {Zabkar, Jure and Mozina, Martin and Bratko, Ivan and Demsar, Janez},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {3195--3199},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/485},
}
TY - CPAPER TI - Learning Qualitative Models from Numerical Data: Extended abstract AU - Zabkar, Jure AU - Mozina, Martin AU - Bratko, Ivan AU - Demsar, Janez T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 3195 EP - 3199 UR - https://ijcai.org/proceedings/2013/485 ER -Copied
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