Using binary classifiers for one-class classification

Kang, Seokho

doi:10.1016/j.eswa.2021.115920

Cited by 18 publications

(5 citation statements)

References 32 publications

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“…In cases of extreme class imbalance associated with obtaining and labelling the positive class, one class classification (OCC), also known as anomaly detection, is a useful alternative as it only requires the presence of negative class examples for training the classifier. Essentially, this unsupervised method learns a decision boundary around the target class (inliers) and identifies instances outside of it as anomalies or outliers [30][31][32][33] . Anomaly detection has been applied to Alzheimer's disease diagnosis 34 , identifying acute myeloid leukemia associated genes 35 , and abnormal skin tissue detection 36 .…”

Section: MLmentioning

confidence: 99%

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers

Yousef,

Feng,

Jelinek

2024

Sci Rep

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Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.

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Section: MLmentioning

confidence: 99%

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers

Yousef,

Feng,

Jelinek

2024

Sci Rep

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“…At test time, the classifier with the highest confidence score is chosen as the predicted class, and another approach is to use a oneversus-one (OVO) strategy, in which a separate binary SVM classifier is trained for every pair of classes, at test time, the class with the most votes from the individual classifiers is chosen as the predicted classes [30]. Another approach is to use a multiclass SVM algorithm; this approach uses a single SVM classifier that is trained to distinguish between all of the classes at once, rather than training multiple binary classifiers [31].…”

Section: Classification With Svmmentioning

confidence: 99%

Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling

Alwan,

Naji

2023

Jour. Kufa Math. Comp.

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The rice crop is one of the most important food crops that depend on it globally. Therefore, farmers must preserve the production of this crop from infection with pests and diseases that lead to its destruction through artificial intelligence and deep learning techniques. A hybrid model combining a Residual Network 50 (ResNet50) deep convolutional neural network (CNN) and a support vector machine (SVM) developed diagnoses rice diseases. Farmers or people working in agriculture could use this model to quickly and accurately identify the diseases in their crops and treat them, increasing crop yield and reducing the need for costly and time-consuming manual inspection. ResNet50, a deep learning model effective at image classification tasks, was used to extract features from images of rice plants. SVM was then used to classify the diseases based on these features. The ResNet50 was able to capture complex patterns in the images, while the SVM was able to use these patterns to make accurate classification decisions. This hybrid model allowed for high precision in rice disease diagnosis, achieving an accuracy of approximately 99%.

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“…e method has produced excellent results on various datasets. Kanag [23] uses the clustering technique to many clusters.…”

Section: Literature Surveymentioning

confidence: 99%

“…The method has produced excellent results on various datasets. Kanag [ 23 ] uses the clustering technique to many clusters. Therese clusters are used using the one-against-rest method to create many binary-classifiers.…”

Section: Literature Surveymentioning

confidence: 99%

One-Class Classification by Ensembles of Random Planes (OCCERPs)

Ahmad

2022

Computational Intelligence and Neuroscience

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One-class classification (OCC) deals with the classification problem in which the training data have data points belonging only to the target class. In this paper, we present a one-class classification algorithm, One-Class Classification by Ensembles of Random Plane (OCCERP), that uses random planes to address OCC problems. OCCERP creates many random planes. There is a pivot point in each random plane. A data point is projected in a random plane and a distance from a pivot point is used to compute the outlier score of the data point. Outlier scores of a point computed using many random planes are combined to get the final outlier score of the point. An extensive comparison of the OCCERP algorithm with state-of-the-art OCC algorithms on several datasets was conducted to show the effectiveness of the proposed approach. The effect of the ensemble size on the performance of the OCCERP algorithm is also studied.

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Using binary classifiers for one-class classification

Cited by 18 publications

References 32 publications

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers

Rice Diseases Classification by Residual Network 50 (RESNET50) and Support Vector Machine (SVM) Modeling

One-Class Classification by Ensembles of Random Planes (OCCERPs)

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