One-Class Classification Approach Using Feature-Slide Prediction Subtask for Feature Data

2021 ◽  
pp. 84-96
Author(s):  
Toshitaka Hayashi ◽  
Hamido Fujita
Keyword(s):  
Classification Approach ◽  
One Class Classification

Mapping raised bogs with an iterative one-class classification approach

2016 ◽  
Vol 120 ◽  
pp. 53-64 ◽  
Author(s):  
Benjamin Mack ◽  
Ribana Roscher ◽  
Stefanie Stenzel ◽  
Hannes Feilhauer ◽  
Sebastian Schmidtlein ◽  
...  
Keyword(s):  
Classification Approach ◽  
Raised Bogs ◽  
One Class Classification

scholarly journals Class-Modeling of Septic Shock With Hyperdimensional Computing

2021 ◽  
Author(s):  
Neftali D Watkinson ◽  
Tony Givargis ◽  
Victor Joe ◽  
Alexandru Nicolau ◽  
Alexander Veidenbaum
Keyword(s):  
Septic Shock ◽  
Immune System ◽  
Critical Time ◽  
Classification Approach ◽  
Acute Change ◽  
Patient State ◽  
Organ Tissue ◽  
Target Priority ◽  
One Class Classification ◽  
Sensitivity Increase

Sepsis arises when a patient's immune system has an extreme reaction to an infection. This is followed by septic shock if damage to organ tissue is so extensive that it causes a total systemic failure. Early detection of septic shock among septic patients could save critical time for preparation and prevention treatment. Due to the high variance in symptoms and patient state before shock, it is challenging to create a protocol that would be effective across patients. However, since septic shock is an acute change in patient state, modeling patient stability could be more effective in detecting a condition that departs from it. In this paper we present a one-class classification approach to septic shock using hyperdimensional computing. We built various models that consider different contexts and can be adapted according to a target priority. Among septic patients, the models can detect septic shock accurately with 90% sensitivity and overall accuracy of 60% of the cases up to three hours before the onset of septic shock, with the ability to adjust predictions according to incoming data. Additionally, the models can be easily adapted to prioritize sensitivity (increase true positives) or specificity (decrease false positives).

Computational Rumor Detection Without Non-Rumor: A One-Class Classification Approach

2019 ◽  
Vol 6 (5) ◽  
pp. 830-846 ◽  
Author(s):  
Amir Ebrahimi Fard ◽  
Majid Mohammadi ◽  
Yang Chen ◽  
Bartel Van de Walle
Keyword(s):  
Classification Approach ◽  
Rumor Detection ◽  
One Class Classification

scholarly journals Fault recognition in smart grids by a one-class classification approach

2014 ◽  
Author(s):  
Enrico De Santis ◽  
Lorenzo Livi ◽  
Fabio Massimo Frattale Mascioli ◽  
Alireza Sadeghian ◽  
Antonello Rizzi
Keyword(s):  
Smart Grids ◽  
Classification Approach ◽  
Fault Recognition ◽  
One Class Classification
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