2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE) (2012)
Larnaca, Cyprus Cyprus
Nov. 11, 2012 to Nov. 13, 2012
Antonis Lambrou , Frederick Research Center, Nicosia, Cyprus
Harris Papadopoulos , Frederick Research Center, Nicosia, Cyprus
Alexander Gammerman , Computer Learning Research Centre, Royal Holloway, University of London, UK
Venn Prediction (VP) is a machine learning framework that can be used to develop methods that provide well-calibrated probabilistic outputs. Unlike other probabilistic methods, the VP framework guarantees validity under the assumption that the data are independently and identically distributed (i.i.d.). Well-calibrated probabilistic outputs are of great importance, especially in biomedical applications. In this work, we develop a new Venn Predictor based on the Sequential Minimal Optimisation (SMO) algorithm and we examine its application to two real-world biomedical problems. We demonstrate in our results that our method can provide calibrated probabilistic outputs for predictions without any loss of accuracy. Moreover, we compare the outputs of our method with the probability outputs of SMO with logistic regression.
Accuracy, Prediction algorithms, Probabilistic logic, Taxonomy, Logistics, Machine learning, Reliability, biomedicine, Venn Prediction, Probability outputs
A. Lambrou, H. Papadopoulos and A. Gammerman, "Calibrated probabilistic predictions for biomedical applications," 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), Larnaca, Cyprus Cyprus, 2012, pp. 211-216.