Boundary‐based Fuzzy‐SVDD for one‐class classification

2021 ◽  
Author(s):  
Dongdong Li ◽  
Xinlei Xu ◽  
Zhe Wang ◽  
Chenjie Cao ◽  
Minguang Wang
Keyword(s):  
One Class Classification

scholarly journals Rust Detection of Steel Structure via One-Class Classification and L2 Sparse Representation with Decision Fusion

2020 ◽  
Vol E103.D (2) ◽  
pp. 450-453
Author(s):  
Guizhong ZHANG ◽  
Baoxian WANG ◽  
Zhaobo YAN ◽  
Yiqiang LI ◽  
Huaizhi YANG
Keyword(s):  
Sparse Representation ◽  
Steel Structure ◽  
Decision Fusion ◽  
One Class Classification

scholarly journals Meta Learning for Few-Shot One-Class Classification

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 195-208
Author(s):  
Gabriel Dahia ◽  
Maurício Pamplona Segundo
Keyword(s):  
Feature Representation ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Target Class ◽  
Feature Representations ◽  
Shot Classification ◽  
Training Stage ◽  
Comparable Performance ◽  
Meta Learning ◽  
One Class Classification

We propose a method that can perform one-class classification given only a small number of examples from the target class and none from the others. We formulate the learning of meaningful features for one-class classification as a meta-learning problem in which the meta-training stage repeatedly simulates one-class classification, using the classification loss of the chosen algorithm to learn a feature representation. To learn these representations, we require only multiclass data from similar tasks. We show how the Support Vector Data Description method can be used with our method, and also propose a simpler variant based on Prototypical Networks that obtains comparable performance, indicating that learning feature representations directly from data may be more important than which one-class algorithm we choose. We validate our approach by adapting few-shot classification datasets to the few-shot one-class classification scenario, obtaining similar results to the state-of-the-art of traditional one-class classification, and that improves upon that of one-class classification baselines employed in the few-shot setting.

scholarly journals A one-class classification decision tree based on kernel density estimation

2020 ◽  
Vol 91 ◽  
pp. 106250 ◽  
Author(s):  
Sarah Itani ◽  
Fabian Lecron ◽  
Philippe Fortemps
Keyword(s):  
Decision Tree ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
One Class Classification

scholarly journals Ensemble Deep Learning for Cervix Image Selection toward Improving Reliability in Automated Cervical Precancer Screening

Diagnostics ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 451 ◽  
Author(s):  
Peng Guo ◽  
Zhiyun Xue ◽  
Zac Mtema ◽  
Karen Yeates ◽  
Ophira Ginsburg ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Digital Camera ◽  
Deep Learning Algorithm ◽  
Average Accuracy ◽  
Acceptable Quality ◽  
Cervical Precancer ◽  
One Class Classification ◽  
Learning Architectures

Automated Visual Examination (AVE) is a deep learning algorithm that aims to improve the effectiveness of cervical precancer screening, particularly in low- and medium-resource regions. It was trained on data from a large longitudinal study conducted by the National Cancer Institute (NCI) and has been shown to accurately identify cervices with early stages of cervical neoplasia for clinical evaluation and treatment. The algorithm processes images of the uterine cervix taken with a digital camera and alerts the user if the woman is a candidate for further evaluation. This requires that the algorithm be presented with images of the cervix, which is the object of interest, of acceptable quality, i.e., in sharp focus, with good illumination, without shadows or other occlusions, and showing the entire squamo-columnar transformation zone. Our prior work has addressed some of these constraints to help discard images that do not meet these criteria. In this work, we present a novel algorithm that determines that the image contains the cervix to a sufficient extent. Non-cervix or other inadequate images could lead to suboptimal or wrong results. Manual removal of such images is labor intensive and time-consuming, particularly in working with large retrospective collections acquired with inadequate quality control. In this work, we present a novel ensemble deep learning method to identify cervix images and non-cervix images in a smartphone-acquired cervical image dataset. The ensemble method combined the assessment of three deep learning architectures, RetinaNet, Deep SVDD, and a customized CNN (Convolutional Neural Network), each using a different strategy to arrive at its decision, i.e., object detection, one-class classification, and binary classification. We examined the performance of each individual architecture and an ensemble of all three architectures. An average accuracy and F-1 score of 91.6% and 0.890, respectively, were achieved on a separate test dataset consisting of more than 30,000 smartphone-captured images.

scholarly journals Towards Application of One-Class Classification Methods to Medical Data

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Itziar Irigoien ◽  
Basilio Sierra ◽  
Concepción Arenas
Keyword(s):  
State Of The Art ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Data Sets ◽  
Biomedical Data ◽  
Vector Data ◽  
Target Class ◽  
Tumor Recognition ◽  
One Class Classification

In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

scholarly journals Fuzzy One-Class Classification Model Using Contamination Neighborhoods

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Lev V. Utkin
Keyword(s):  
Risk Measures ◽  
Synthetic Data ◽  
Fuzzy Model ◽  
Fuzzy Classification ◽  
Classification Model ◽  
Support Vector ◽  
Standard Support Vector Machine ◽  
Robust Model ◽  
Data Points ◽  
One Class Classification

A fuzzy classification model is studied in the paper. It is based on the contaminated (robust) model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. Experimental results with synthetic data illustrate the proposed fuzzy model.

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