使用支持向量数据描述（ SVDD ）进行异常检测的MATLAB代码。.zip

<p align="center"> <img src="http://github-files-qiu.oss-cn-beijing.aliyuncs.com/SVDD-MATLAB/demo_.png"> </p> <h3 align="center">Support Vector Data Description (SVDD)</h3> <p align="center">MATLAB Code for abnormal detection using SVDD</p> <p align="center">Version 2.2, 13-MAY-2022</p> <p align="center">Email: iqiukp@outlook.com</p> <div align=center> [](https://ww2.mathworks.cn/matlabcentral/fileexchange/69296-support-vector-data-description-svdd) <img src="https://img.shields.io/github/v/release/iqiukp/Support-Vector-Data-Description-SVDD?label=version" /> <img src="https://img.shields.io/github/repo-size/iqiukp/Support-Vector-Data-Description-SVDD" /> <img src="https://img.shields.io/github/languages/code-size/iqiukp/Support-Vector-Data-Description-SVDD" /> <img src="https://img.shields.io/github/languages/top/iqiukp/Support-Vector-Data-Description-SVDD" /> <img src="https://img.shields.io/github/stars/iqiukp/Support-Vector-Data-Description-SVDD" /> <img src="https://img.shields.io/github/forks/iqiukp/Support-Vector-Data-Description-SVDD" /> </div> <hr /> ## ✨ MAIN FEATURES - SVDD model for one-class or binary classification - Multiple kinds of kernel functions (linear, gaussian, polynomial, sigmoid, laplacian) - Visualization of decision boundaries for 2D or 3D data - Parameter optimization using Bayesian optimization, genetic algorithm, and pParticle swarm optimization - Weighted SVDD model - Hybrid-kernel SVDD model (K =w1×K1+w2×K2+...+wn×Kn) ## ⚠️ NOTICES - This version of this code is not compatible with the versions lower than ***R2016b***. - The label must be 1 for positive sample or -1 for negative sample. - Detailed applications please see the provided ***demonstrations***. - This code is for reference only. ## 🔨 HOW TO USE ### 👉 A simple SVDD model Please see the demonstration [`📝 demo_BasicSVDD.m`](https://github.com/iqiukp/SVDD-MATLAB/blob/master/demo_BasicSVDD.m) for details. ```MATLAB % generate dataset ocdata = BinaryDataset(); ocdata.generate; [trainData, trainLabel, testData, testLabel] = ocdata.partition; % set parameter kernel = BaseKernel('type', 'gaussian', 'gamma', 0.04); cost = 0.3; svddParameter = struct('cost', cost,... 'kernelFunc', kernel); % creat an SVDD object svdd = BaseSVDD(svddParameter); % train SVDD model svdd.train(trainData, trainLabel); % test SVDD model results = svdd.test(testData, testLabel); ``` - `BinaryDataset` is designed to validate the svdd model only, you can use your data and please be careful to keep the naming of variables consistent, e.g. `trainData`, `trainLabel`, `testData`, and `testLabel`. - Specifically, if the data does not have labels, please change the inputs for training or testing to `svdd.train(trainData)` and `results = svdd.test(testData)`. ### 👉 Parameter Optimization for SVDD model A class named `SvddOptimization` is defined to optimized the parameters. First define an optimization setting structure, then add it to the svdd parameter structure.The parameter optimization of the polynomial kernel function can only be achieved by using Bayesian optimization. Please see the demonstration [`📝 demo_ParameterOptimization.m`](https://github.com/iqiukp/SVDD-MATLAB/blob/master/demo_ParameterOptimization.m) for details. ```MATLAB % optimization setting optimization.method = 'bayes'; % optimization.maxIteration = 20; optimization.display = 'on'; % SVDD parameter svddParameter = struct('cost', cost,... 'kernelFunc', kernel,... 'optimization', optimization); ``` The full properties of optimization are - `method`: optimization methods, only supported for 'bayes', 'pso', and 'ga'. - `variableName`: variables that are to be optimized, including 'cost', 'degree', 'offset', and 'gamma'. - `variableType`: variable type, specified as 'real' (real variable), 'integer' (integer variable). - `lowerBound`: lower bound of variables. - `upperBound`: upper bound of variables. - `maxIteration`: max iterations. - `points`: size of group or seed. - `display `: visualization, 'on' or 'off'. ### 👉 Visualization of SVDD model A class named `SvddVisualization` is defined to visualize the training and test results. Based on the trained SVDD model, the ROC curve of the training results (only supported for dataset containing both positive and negetive samples) is ```MATLAB % Visualization svplot = SvddVisualization(); svplot.ROC(svdd); ``` <p align="center"> <img src="http://github-files-qiu.oss-cn-beijing.aliyuncs.com/SVDD-MATLAB/ROC-3D_.png"> </p> The decision boundaries (only supported for 2D/3D dataset) are ```MATLAB % Visualization svplot = SvddVisualization(); svplot.boundary(svdd); ``` <p align="center"> <img src="http://github-files-qiu.oss-cn-beijing.aliyuncs.com/SVDD-MATLAB/boundary-2D_.png"> </p> <p align="center"> <img src="https://github-files-qiu.oss-cn-beijing.aliyuncs.com/SVDD-MATLAB/boundary-3D_.png"> </p> The distance between the test data and the hypersphere is ```MATLAB svplot.distance(svdd, results); ``` <p align="center"> <img src="http://github-files-qiu.oss-cn-beijing.aliyuncs.com/SVDD-MATLAB/distance-3D_.png"> </p> ### 👉 Binary Dataset for SVDD model A class named `BinaryDataset` is defined to generate and partition the 2D or 3D binary dataset. Please see the demonstration [`📝demo_BinaryDataset.m`](https://github.com/iqiukp/SVDD-MATLAB/blob/master/demo_BinaryDataset.m) for details. ```MATLAB ocdata = BinaryDataset(); [data, label] = ocdata.generate; [trainData, trainLabel, testData, testLabel] = ocdata.partition; ``` The method `generate` is designed to generate dataset. The syntax of `generate` is ```MATLAB ocdata.generate; data = ocdata.generate; [data, label] = ocdata.generate; ``` The method `partition` is designed to partition dataset into training dataset and test dataset. The syntax of `partition` is ```MATLAB [trainData, trainLabel, testData, testLabel] = ocdata.partition; ``` The full Name-Value Arguments of class `BinaryDataset` are - `shape`: shape of dataset, 'banana' or 'circle'. - `dimensionality`: dimensionality of dataset, 2 or 3. - `number`: number of samples per class, for example: [200, 200]. - `display`: visualization, 'on' or 'off'. - `noise`: noise added to dataset with range [0, 1]. For example: 0.2. - `ratio`: ratio of the test set with range (0, 1). For example: 0.3. ### 👉 Kernel funcions A class named `BaseKernel` is defined to compute kernel function matrix. Please see the demonstration [`📝demo_KernelFunction.m`](https://github.com/iqiukp/SVDD-MATLAB/blob/master/demo_KernelFunction.m) for details. ```MATLAB %{ type - linear : k(x,y) = x'*y polynomial : k(x,y) = (γ*x'*y+c)^d gaussian : k(x,y) = exp(-γ*||x-y||^2) sigmoid : k(x,y) = tanh(γ*x'*y+c) laplacian : k(x,y) = exp(-γ*||x-y||) degree - d offset - c gamma - γ %} kernel = BaseKernel('type', 'gaussian', 'gamma', value); kernel = BaseKernel('type', 'polynomial', 'degree', value); kernel = BaseKernel('type', 'linear'); kernel = BaseKernel('type', 'sigmoid', 'gamma', value); kernel = BaseKernel('type', 'laplacian', 'gamma', value); ``` ### 👉 Cross Validation In this code, two cross-validation methods are supported: 'K-Folds' and 'Holdout'. For example, the cross-validation of 5-Folds is ```MATLAB svddParameter = struct('cost', cost,... 'kernelFunc', kernel,... 'KFold', 5); ``` For example, the cross-validation of the Holdout method with a ratio of 0.3 is ```MATLAB svddParameter = struct('cost', cost,... 'kernelFunc', kernel,... 'Holdout', 0.3); ``` ### 👉 Dimensionality reduction using PCA For example, reducing