J. I. Segovia Martín, S. Mazuelas
Supervised learning can enable multiple important medical applications such as the prognosis of COVID-19 infections. These scenarios are often affected by a covariate shift, in which the marginal distributions of covariates of training and testing samples are different, but the label conditionals coincide. For instance, for the COVID-19 prognosis, predicting one wave using data collected in previous waves requires carrying out covariate shift adaptation that accounts for changes in the health data. The methods presented are based on minimax risk classifiers (MRCs) and avoid the limitations of existing weighting methods for covariate shift adaptation by using a double weighting for both training and testing samples. We develop effective techniques that obtain both sets of weights and generalize the conventional kernel mean matching method. The proposed method also achieves enhanced classification performance in experiments carried out with both synthetic and medical datasets.
Keywords: Covariate Shift, Supervised Classification, Selection Bias, Minimax Classification
Scheduled
Data Analysis
November 8, 2023 5:20 PM
HC3: Canónigos Room 3