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Found 2 papers, 0 papers with code
MUTE-Reco: MUTual Information Assisted Ensemble Feature RECOmmender System for Healthcare Prognosis
The performance of the proposed recommender is compared with four state-of-the-art methods using recommender systems' performance metrics like average precision@K, precision@K, recall@K, F1@K, reciprocal rank@K. Experimental results show that the model built with the recommended features can attain a higher accuracy (96. 6% and 98. 6% using support vector machine and neural network, respectively) for classifying different stages of ccRCC with a reduced feature set as compared to existing methods.
Mixed Uncertainty Sets for Robust Combinatorial Optimization
In an extensive computational study using a shortest path problem based on real-world data, we auto-tune uncertainty sets to the available data, and show that with regard to out-sample performance, the combination of multiple sets can give better results than each set on its own.
