27 Support Vector Machines
Learn how to use Support Vector Machines, a powerful algorithm based on statistical learning theory.
Oracle Data Mining implements Support Vector Machines for Classification, Regression, and Anomaly Detection.
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See Also:
Milenova, B.L., Yarmus, J.S., Campos, M.M., "Support Vector Machines in Oracle Database 10g: Removing the Barriers to Widespread Adoption of Support Vector Machines", Proceedings of the 31st VLDB Conference, Trondheim, Norway, 2005.
27.1 About Support Vector Machines
Support Vector Machine (SVM) is a powerful, state-of-the-art algorithm with strong theoretical foundations based on the Vapnik-Chervonenkis theory. SVM has strong regularization properties. Regularization refers to the generalization of the model to new data.
27.1.1 Advantages of SVM
Oracle Data Mining SVM implementation includes two types of solvers, an Interior Point Method (IPM) solver and a Sub-Gradient Descent (SGD) solver. The IPM solver provides very stable and accurate solutions, however, it may not be able to handle data of very high dimensionality. For high dimensional data, for example, text, ratings, and so on, the SGD solver is a better choice. Both solvers have highly scalable parallel implementations and can handle large volumes of data.
27.1.2 Advantages of SVM in Oracle Data Mining
Oracle Data Mining has its own proprietary implementation of Support Vector Machines (SVM), which exploits the many benefits of the algorithm while compensating for some of the limitations inherent in the SVM framework. Oracle Data Mining SVM provides the scalability and usability that are needed in a production quality data mining system.
27.1.2.1 Usability
Explains usability for Support Vector Machines (SVM) in Oracle Data Mining.
Usability is a major enhancement, because SVM has often been viewed as a tool for experts. The algorithm typically requires data preparation, tuning, and optimization. Oracle Data Mining minimizes these requirements. You do not need to be an expert to build a quality SVM model in Oracle Data Mining. For example:
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Data preparation is not required in most cases.
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Default tuning parameters are generally adequate.
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27.1.2.2 Scalability
Learn how to scale the data for Support Vector Machines (SVM).
When dealing with very large data sets, sampling is often required. However, sampling is not required with Oracle Data Mining SVM, because the algorithm itself uses stratified sampling to reduce the size of the training data as needed.
Oracle Data Mining SVM is highly optimized. It builds a model incrementally by optimizing small working sets toward a global solution. The model is trained until convergence on the current working set, then the model adapts to the new data. The process continues iteratively until the convergence conditions are met. The Gaussian kernel uses caching techniques to manage the working sets.
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27.1.3 Kernel-Based Learning
Learn about kernal-based functions to transform the input data for Support Vector Machines (SVM).
SVM is a kernel-based algorithm. A kernel is a function that transforms the input data to a high-dimensional space where the problem is solved. Kernel functions can be linear or nonlinear.
Oracle Data Mining supports linear and Gaussian (nonlinear) kernels.
In Oracle Data Mining, the linear kernel function reduces to a linear equation on the original attributes in the training data. A linear kernel works well when there are many attributes in the training data.
The Gaussian kernel transforms each case in the training data to a point in an n-dimensional space, where n is the number of cases. The algorithm attempts to separate the points into subsets with homogeneous target values. The Gaussian kernel uses nonlinear separators, but within the kernel space it constructs a linear equation.
Note:
Active Learning is not relevant in Oracle Database 12c Release 2 and later. A setting similar to Active Learning is ODMS_SAMPLING.
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27.2 Tuning an SVM Model
Learn about configuring settings for Support Vector Machines (SVM).
SVM have built-in mechanisms that automatically choose appropriate settings based on the data. You may need to override the system-determined settings for some domains.
Settings pertain to regression, classification, and anomaly detection unless otherwise specified.
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27.3 Data Preparation for SVM
The Support Vector Machines (SVM) algorithm operates natively on numeric attributes. SVM uses z-score normalization on numeric attributes. The normalization occurs only for two-dimensional numeric columns (not nested). The algorithm automatically "explodes" categorical data into a set of binary attributes, one per category value. For example, a character column for marital status with values married or single is transformed to two numeric attributes: married and single. The new attributes can have the value 1 (true) or 0 (false).
When there are missing values in columns with simple data types (not nested), SVM interprets them as missing at random. The algorithm automatically replaces missing categorical values with the mode and missing numerical values with the mean.
When there are missing values in the nested columns, SVM interprets them as sparse. The algorithm automatically replaces sparse numerical data with zeros and sparse categorical data with zero vectors.
27.3.1 Normalization
Support Vector Machines require the normalization of numeric input. Normalization places the values of numeric attributes on the same scale and prevents attributes with a large original scale from biasing the solution. Normalization also minimizes the likelihood of overflows and underflows.
27.3.2 SVM and Automatic Data Preparation
Learn about treating and transforming data manually or through Automatic Data Preparation (ADP) for Support Vector Machines (SVM).
The SVM algorithm automatically handles missing value treatment and the transformation of categorical data, but normalization and outlier detection must be handled by Automatic Data Preparation (ADP) or prepared manually. ADP performs min-max normalization for SVM.
Note:
Oracle recommends that you use Automatic Data Preparation with SVM. The transformations performed by ADP are appropriate for most models.
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27.4 SVM Classification
Support Vector Machines (SVM) Classification is based on the concept of decision planes that define decision boundaries. A decision plane is one that separates between a set of objects having different class memberships. SVM finds the vectors ("support vectors") that define the separators giving the widest separation of classes.
SVM classification supports both binary, multiclass, and multitarget Classification. Multitarget alllows multiple class labels to be associated with a single row. The target type is a collection of type ORA_MINING_VARCHAR2_NT.
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27.4.1 Class Weights
Learn when to implement class weights to a data in Support Vector Machines (SVM).
In SVM classification, weights are a biasing mechanism for specifying the relative importance of target values (classes).
SVM models are automatically initialized to achieve the best average prediction across all classes. However, if the training data does not represent a realistic distribution, you can bias the model to compensate for class values that are under-represented. If you increase the weight for a class, then the percent of correct predictions for that class must increase.
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27.5 One-Class SVM
Oracle Data Mining uses Support Vector Machines (SVM) as the one-class classifier for anomaly detection. When SVM is used for anomaly detection, it has the classification mining function but no target.
One-class SVM models, when applied, produce a prediction and a probability for each case in the scoring data. If the prediction is 1, the case is considered typical. If the prediction is 0, the case is considered anomalous. This behavior reflects the fact that the model is trained with normal data.
You can specify the percentage of the data that you expect to be anomalous with the SVMS_OUTLIER_RATE build setting. If you have some knowledge that the number of "suspicious" cases is a certain percentage of your population, then you can set the outlier rate to that percentage. The model approximately identifies that many "rare" cases when applied to the general population.
27.6 SVM Regression
Learn how to use epsilon-insensitivity loss function to solve regression problems in Support Vector Machines (SVM).
SVM uses an epsilon-insensitive loss function to solve regression problems.
SVM regression tries to find a continuous function such that the maximum number of data points lie within the epsilon-wide insensitivity tube. Predictions falling within epsilon distance of the true target value are not interpreted as errors.
The epsilon factor is a regularization setting for SVM regression. It balances the margin of error with model robustness to achieve the best generalization to new data.
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