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The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations.

, , , , and . IACR Trans. Cryptogr. Hardw. Embed. Syst., 2019 (1): 209-237 (2019)

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A review of feature selection methods with applications., , and . MIPRO, page 1200-1205. IEEE, (2015)Feature set extension for heart rate variability analysis by using non-linear, statistical and geometric measures., and . ITI, page 35-40. IEEE, (2009)Improving Side-Channel Analysis Through Semi-supervised Learning., , , , and . CARDIS, volume 11389 of Lecture Notes in Computer Science, page 35-50. Springer, (2018)Heart failure ontology., , and . Bio-Algorithms and Med-Systems, 7 (2): 101-110 (2011)Extension and detailed overview of the HRVFrame framework for heart rate variability analysis., , and . EUROCON, page 1757-1763. IEEE, (2013)The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations., , , , and . IACR Trans. Cryptogr. Hardw. Embed. Syst., 2019 (1): 209-237 (2019)The secrets of profiling for side-channel analysis: feature selection matters., , , , and . IACR Cryptology ePrint Archive, (2017)The Curse of Class Imbalance and Conflicting Metrics with Machine Learning for Side-channel Evaluations., , , , and . IACR Cryptology ePrint Archive, (2018)Parallelization in biomedical time series analysis web platform: The MULTISAB project experience., , , , and . MIPRO, page 313-318. IEEE, (2018)HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm., and . AIME, volume 6747 of Lecture Notes in Computer Science, page 96-100. Springer, (2011)