A supervised machine learning algorithm for arrhythmia analysis

By H. A. Guvenir, Burak Açar, Gulsen Demiroz, A. Çekin. 1997

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “D7: Arrhythmia The Arrhythmia dataset (Guvenir et al., 1997) from the UCI Machine Learning Repository (Bache and Lichman, 2013) concerns cardiac arrhythmia.”
HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection (2003, background): “The second task is clinical diagnosis of arrhythmia into 8 possible disease categories on the basis of clinical and EKG data [5].”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, background): “D7: Arrhythmia The Arrhythmia dataset (Guvenir et al., 1997) from the UCI Machine Learning Repository (Bache and Lichman, 2013) concerns cardiac arrhythmia.”
Interval Coded Scoring extensions for larger problems (2017, methodology): “The details of the features can be found in [18].”
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data (2017, unknown): “…for defending against e.g., commonly required for fault tolerant systems , are challenging to Moreover, adversarial learning literature ( e.g., [9, 17, 19]) data-driven CPS, it is necessary to consider techniques for resilient machine learning that can defend against cyber-physical attacks”