Anomaly Detection using SIngle Class SVM with Gaussian Kernel
-
Upload
anoop-vasant-kumar -
Category
Data & Analytics
-
view
79 -
download
1
Transcript of Anomaly Detection using SIngle Class SVM with Gaussian Kernel
![Page 1: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/1.jpg)
Method to Improve Breast Cancer
Diagnosis
Anoop Vasant Kumar
![Page 2: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/2.jpg)
ProblemPreventable medical error is a big
killer.In the US alone, 400,000 people die
every year due to avoidable medical error in hospitals - this is equivalent to
TWO JUMBO JETS crashing every single day!
--NHS sets aside 26.1 Billion Dollars
to settle outstanding negligences and liabilities in clinical safety.
![Page 3: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/3.jpg)
Causes of Avoidable Medical Errors
Procedures and training methods not reformed, so mistakes happen again and again.
![Page 4: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/4.jpg)
Features of the Dataset - Labelled 699 clinical cases
Nine real-valued features are chosen for each cell nucleus:a) radius (mean of distances from center to points on the perimeter)b) texture (standard deviation of gray-scale values)c) perimeterd) areae) smoothness (local variation in radius lengths)f) compactness (perimeter^2 / area - 1.0)g) concavity (severity of concave portions of the contour)h) concave points (number of concave portions of the contour)i) symmetry
![Page 5: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/5.jpg)
Some approaches to solving anomaly detection problem
![Page 6: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/6.jpg)
683 cases of labelled data - benign/malignant
Imbalanced dataset
![Page 7: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/7.jpg)
Spot check feature histograms
![Page 8: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/8.jpg)
Visualizing Classification Classification using Logistic Regression achieved an F1 score of 0.95 on the anomaly class.
Classification by model on unseen data
Actual data
![Page 9: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/9.jpg)
KNN - non parametric model to verify classification
Scaled feature vector
Identified precise k value using elbow methodFor k = 3We had an F1 score 0.95 for the anomaly class
![Page 10: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/10.jpg)
Unsupervised Anomaly Detection using SVM - Gaussian Kernel Trick
1)Objective is to train a one class svm gaussian hypersphere that quarantines the benign cells.
2)Dropped labels from dataset and is split into benign and malignant datasets.
3)Benign dataset is used to train the model.
4)Malignant dataset, the dataset that contains the outliers is used to test.
5)A single class SVM is trained with a low gamma value, that captures the influence of training examples on classification.
![Page 11: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/11.jpg)
Gaussian Distribution for benign and malignant cells
Benign multivariate gaussian Malignant multivariate gaussian
![Page 12: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/12.jpg)
![Page 13: Anomaly Detection using SIngle Class SVM with Gaussian Kernel](https://reader034.fdocuments.net/reader034/viewer/2022042702/5886f8dd1a28ab4e3a8b5017/html5/thumbnails/13.jpg)
Single class SVM with gaussian trick - 100% Accuracy