Open Set Subject Classification of Text Documents in Polish by Doc-to-Vec and Local Outlier Factor

2019 ◽  
pp. 455-463
Author(s):  
Tomasz Walkowiak ◽  
Szymon Datko ◽  
Henryk Maciejewski
Keyword(s):  
Subject Classification ◽  
Text Documents ◽  
Local Outlier Factor ◽  
Open Set ◽  
Local Outlier

Algorithm based on modified angle-based outlier factor for open-set classification of text documents

2018 ◽  
Vol 34 (5) ◽  
pp. 718-729 ◽  
Author(s):  
Tomasz Walkowiak ◽  
Szymon Datko ◽  
Henryk Maciejewski
Keyword(s):  
Text Documents ◽  
Open Set

scholarly journals Mosquito species identification using convolutional neural networks with a multitiered ensemble model for novel species detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adam Goodwin ◽  
Sanket Padmanabhan ◽  
Sanchit Hira ◽  
Margaret Glancey ◽  
Monet Slinowsky ◽  
...  
Keyword(s):  
Neural Networks ◽  
Species Identification ◽  
Convolutional Neural Networks ◽  
Novel Species ◽  
Novelty Detection ◽  
Mosquito Species ◽  
Image Database ◽  
Closed Set ◽  
Open Set

AbstractWith over 3500 mosquito species described, accurate species identification of the few implicated in disease transmission is critical to mosquito borne disease mitigation. Yet this task is hindered by limited global taxonomic expertise and specimen damage consistent across common capture methods. Convolutional neural networks (CNNs) are promising with limited sets of species, but image database requirements restrict practical implementation. Using an image database of 2696 specimens from 67 mosquito species, we address the practical open-set problem with a detection algorithm for novel species. Closed-set classification of 16 known species achieved 97.04 ± 0.87% accuracy independently, and 89.07 ± 5.58% when cascaded with novelty detection. Closed-set classification of 39 species produces a macro F1-score of 86.07 ± 1.81%. This demonstrates an accurate, scalable, and practical computer vision solution to identify wild-caught mosquitoes for implementation in biosurveillance and targeted vector control programs, without the need for extensive image database development for each new target region.

scholarly journals Out-of-Distribution Detection for Deep Neural Networks With Isolation Forest and Local Outlier Factor

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 132980-132989
Author(s):  
Siyu Luan ◽  
Zonghua Gu ◽  
Leonid B. Freidovich ◽  
Lili Jiang ◽  
Qingling Zhao
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Local Outlier Factor ◽  
Local Outlier ◽  
Isolation Forest
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