A New One-Class Classification Method Based on Symbolic Representation: Application to Document Classification

Author(s):  
Fahimeh Alaei ◽  
Nathalie Girard ◽  
Sabine Barrat ◽  
Jean-Yves Ramel
2018 ◽  
Vol 11 (4) ◽  
pp. 97-112
Author(s):  
Jeong-Joon Kim ◽  
Yong-Soo Lee ◽  
Jin-Yong Moon ◽  
Jeong-Min Park

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2690 ◽  
Author(s):  
Jannat Yasmin ◽  
Santosh Lohumi ◽  
Mohammed Raju Ahmed ◽  
Lalit Mohan Kandpal ◽  
Mohammad Akbar Faqeerzada ◽  
...  

The feasibility of a color machine vision technique with the one-class classification method was investigated for the quality assessment of tomato seeds. The health of seeds is an important quality factor that affects their germination rate, which may be affected by seed contamination. Hence, segregation of healthy seeds from diseased and infected seeds, along with foreign materials and broken seeds, is important to improve the final yield. In this study, a custom-built machine vision system containing a color camera with a white light emitting diode (LED) light source was adopted for image acquisition. The one-class classification method was used to identify healthy seeds after extracting the features of the samples. A significant difference was observed between the features of healthy and infected seeds, and foreign materials, implying a certain threshold. The results indicated that tomato seeds can be classified with an accuracy exceeding 97%. The infected tomato seeds indicated a lower germination rate (<10%) compared to healthy seeds, as confirmed by the organic growing media germination test. Thus, identification through image analysis and rapid measurement were observed as useful in discriminating between the quality of tomato seeds in real time.


2007 ◽  
Vol Volume 6, april 2007, joint... ◽  
Author(s):  
Oleksiy Mazhelis

International audience One-class classifiers employing for training only the data from one class are justified when the data from other classes is difficult to obtain. In particular, their use is justified in mobile-masquerader detection, where user characteristics are classified as belonging to the legitimate user class or to the impostor class, and where collecting the data originated from impostors is problematic. This paper systematically reviews various one-class classification methods, and analyses their suitability in the context of mobile-masquerader detection. For each classification method, its sensitivity to the errors in the training set, computational requirements, and other characteristics are considered. After that, for each category of features used in masquerader detection, suitable classifiers are identified.


2000 ◽  
Vol 126 (1-4) ◽  
pp. 57-70 ◽  
Author(s):  
Masao Fuketa ◽  
Sangkon Lee ◽  
Takako Tsuji ◽  
Makoto Okada ◽  
Jun-ichi Aoe

2015 ◽  
Vol 7 (8) ◽  
pp. 10143-10163 ◽  
Author(s):  
Bo Wan ◽  
Qinghua Guo ◽  
Fang Fang ◽  
Yanjun Su ◽  
Run Wang

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