Publications

@article{alaiz-rodriguez_minimax_2007,

title = {Minimax regret classifier for imprecise class distributions},

author = {Rocío Alaiz-Rodríguez and Alicia Guerrero-Curieses and Jesús Cid-Sueiro},

url = {https://www.jmlr.org/papers/volume8/alaiz-rodriguez07a/alaiz-rodriguez07a.pdf},

year  = {2007},

date = {2007-01-01},

journal = {Journal of Machine Learning Research},

volume = {8},

number = {4},

pages = {103–130},

abstract = {This paper addresses the challenge of designing a classifier when the stationarity assumption—i.e., the agreement between training and test conditions—does not hold in real-world applications. In these cases, misclassification costs and data generation processes may differ between training and testing. The paper proposes a minimax regret (minimax deviation) approach to classifier design, aiming to minimize the maximum deviation from the optimal risk classifier's performance. Unlike traditional minimax methods, which can lead to severe performance degradation, this approach offers a more robust classification without significant loss of accuracy. The paper presents a neural-based minimax regret classifier for multi-class decision problems and demonstrates its robustness through experimental results.},

keywords = {Classification, Imprecise Class Distribution, Minimax Deviation, Minimax Regret, neural networks},

pubstate = {published},

tppubtype = {article}

}