Classification
In a classification problem, we would typically have some input vectors x and some desired output labels y. Let's consider then a simple classification problem called the yin-yang problem. In this problem, we have two classes of elements. Elements belonging to the positive class, shown in blue; and elements belonging to the negative class, shown in red.
This data can be downloaded in Excel format here. In order to load this data into an application, let's use the ExcelReader class together with some extensions methods from the Accord.Math namespace. Add the following using namespace clauses on top of your source file:
using Accord.Controls;
using Accord.IO;
using Accord.Math;
using Accord.Statistics.Distributions.Univariate;
using Accord.MachineLearning.Bayes;Then, let's write the following code:
// Read the Excel worksheet into a DataTable
DataTable table = new ExcelReader("examples.xls").GetWorksheet("Classification - Yin Yang");
// Convert the DataTable to input and output vectors
double[][] inputs = table.ToJagged<double>("X", "Y");
int[] outputs = table.Columns["G"].ToArray<int>();
// Plot the data
ScatterplotBox.Show("Yin-Yang", inputs, outputs).Hold();After we run and execute this code, we will get the following scatter plot shown on the screen:
Naive Bayes classifiers are simple probabilistic classifiers based on Bayes' theorem with strong independence assumptions between the features.
// In our problem, we have 2 classes (samples can be either
// positive or negative), and 2 inputs (x and y coordinates).
// Create a Naive Bayes learning algorithm
var teacher = new NaiveBayesLearning<NormalDistribution>();
// Use the learning algorithm to learn
var nb = teacher.Learn(inputs, outputs);
// At this point, the learning algorithm should have
// figured important details about the problem itself:
int numberOfClasses = nb.NumberOfClasses; // should be 2 (positive or negative)
int nunmberOfInputs = nb.NumberOfInputs; // should be 2 (x and y coordinates)
// Classify the samples using the model
int[] answers = nb.Decide(inputs);
// Plot the results
ScatterplotBox.Show("Expected results", inputs, outputs);
ScatterplotBox.Show("Naive Bayes results", inputs, answers)
.Hold();
SVMs are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.
In the Linear SVM the idea is to design a hyperplane that classifies the training vectors in two classes.
// Create a L2-regularized L2-loss optimization algorithm for
// the dual form of the learning problem. This is *exactly* the
// same method used by LIBLINEAR when specifying -s 1 in the
// command line (i.e. L2R_L2LOSS_SVC_DUAL).
//
var teacher = new LinearCoordinateDescent();
// Teach the vector machine
var svm = teacher.Learn(inputs, outputs);
// Classify the samples using the model
bool[] answers = svm.Decide(inputs);
// Convert to Int32 so we can plot:
int[] zeroOneAnswers = answers.ToZeroOne();
// Plot the results
ScatterplotBox.Show("Expected results", inputs, outputs);
ScatterplotBox.Show("LinearSVM results", inputs, zeroOneAnswers);
// Grab the index of multipliers higher than 0
int[] idx = teacher.Lagrange.Find(x => x > 0);
// Select the input vectors for those
double[][] sv = inputs.Get(idx);
// Plot the support vectors selected by the machine
ScatterplotBox.Show("Support vectors", sv).Hold();
Kernel methods enable them to operate in high-dimensional, implicit feature space without ever computing the coordinates of the data in that space.
// Create a new Sequential Minimal Optimization (SMO) learning
// algorithm and estimate the complexity parameter C from data
var teacher = new SequentialMinimalOptimization<Gaussian>()
{
UseComplexityHeuristic = true,
UseKernelEstimation = true // estimate the kernel from the data
};
// Teach the vector machine
var svm = teacher.Learn(inputs, outputs);
// Classify the samples using the model
bool[] answers = svm.Decide(inputs);
// Convert to Int32 so we can plot:
int[] zeroOneAnswers = answers.ToZeroOne();
// Plot the results
ScatterplotBox.Show("Expected results", inputs, outputs);
ScatterplotBox.Show("GaussianSVM results", inputs, zeroOneAnswers);
// Grab the index of multipliers higher than 0
int[] idx = teacher.Lagrange.Find(x => x > 0);
// Select the input vectors for those
double[][] sv = inputs.Get(idx);
// Plot the support vectors selected by the machine
ScatterplotBox.Show("Support vectors", sv).Hold();
The standard SVMs are only binary classifiers, meaning they can only classify between two types of classes: 1 or 0, true or false, etc. However, they can be extended to work with multi-class or multi-label classification problems using special constructions. In the framework, those constructions are the OneVsOne and OneVsRest strategies for multiple classes classification, respectively. They can be used to learn multi-class SVMs through the MulticlassSupportVectorLearning or MultilabelSupportVectorLearning classes.
// The following is simple auto association function where each input correspond
// to its own class. This problem should be easily solved by a Linear kernel.
// Sample input data
double[][] inputs =
{
new double[] { 0 },
new double[] { 3 },
new double[] { 1 },
new double[] { 2 },
};
// Outputs for each of the inputs
int[] outputs =
{
0,
3,
1,
2,
};
// Create the Multi-label learning algorithm for the machine
var teacher = new MulticlassSupportVectorLearning<Linear>()
{
Learner = (p) => new SequentialMinimalOptimization<Linear>()
{
Complexity = 10000.0 // Create a hard SVM
}
};
// Learn a multi-label SVM using the teacher
var svm = teacher.Learn(inputs, outputs);
// Compute the machine answers for the inputs
int[] answers = svm.Decide(inputs);This is exactly the same example as above, but rather than having only one output class associated with each input vector, we can have as many classes as we want.
// The following is simple auto association function where each input correspond
// to its own class. This problem should be easily solved by a Linear kernel.
// Sample input data
double[][] inputs =
{
new double[] { 0 },
new double[] { 3 },
new double[] { 1 },
new double[] { 2 },
};
// Outputs for each of the inputs
int[][] outputs =
{
new[] { -1, 1, -1 },
new[] { -1, -1, 1 },
new[] { 1, 1, -1 },
new[] { -1, -1, -1 },
};
// Create the Multi-label learning algorithm for the machine
var teacher = new MultilabelSupportVectorLearning<Linear>()
{
Learner = (p) => new SequentialMinimalOptimization<Linear>()
{
Complexity = 10000.0 // Create a hard SVM
}
};
// Learn a multi-label SVM using the teacher
var svm = teacher.Learn(inputs, outputs);
// Compute the machine answers for the inputs
bool[][] answers = svm.Decide(inputs);
// Use the machine as if it were a multi-class machine
// instead of a multi-label, identifying the strongest
// class among the multi-label predictions:
int[] maxAnswers = svm.ToMulticlass().Decide(inputs);The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
// In our problem, we have 2 classes (samples can be either
// positive or negative), and 2 continuous-valued inputs.
C45Learning teacher = new C45Learning(new[] {
DecisionVariable.Continuous("X"),
DecisionVariable.Continuous("Y")
});
// Use the learning algorithm to induce the tree
DecisionTree tree = teacher.Learn(inputs, outputs);
// Classify the samples using the model
int[] answers = tree.Decide(inputs);
// Plot the results
ScatterplotBox.Show("Expected results", inputs, outputs);
ScatterplotBox.Show("Decision Tree results", inputs, answers)
.Hold();
The word network in the term 'artificial neural network' refers to the inter–connections between the neurons in the different layers of each system.
// Since we would like to learn binary outputs in the form
// [-1,+1], we can use a bipolar sigmoid activation function
IActivationFunction function = new BipolarSigmoidFunction();
// In our problem, we have 2 inputs (x, y pairs), and we will
// be creating a network with 5 hidden neurons and 1 output:
//
var network = new ActivationNetwork(function,
inputsCount: 2, neuronsCount: new[] { 5, 1 });
// Create a Levenberg-Marquardt algorithm
var teacher = new LevenbergMarquardtLearning(network)
{
UseRegularization = true
};
// Because the network is expecting multiple outputs,
// we have to convert our single variable into arrays
//
var y = outputs.ToDouble().ToArray();
// Iterate until stop criteria is met
double error = double.PositiveInfinity;
double previous;
do
{
previous = error;
// Compute one learning iteration
error = teacher.RunEpoch(inputs, y);
} while (Math.Abs(previous - error) < 1e-10 * previous);
// Classify the samples using the model
int[] answers = inputs.Apply(network.Compute).GetColumn(0).Apply(System.Math.Sign);
// Plot the results
ScatterplotBox.Show("Expected results", inputs, outputs);
ScatterplotBox.Show("Network results", inputs, answers)
.Hold();
See Resilient Backpropagation.
Logistic regression measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function.
// Create iterative re-weighted least squares for logistic regressions
var teacher = new IterativeReweightedLeastSquares<LogisticRegression>()
{
MaxIterations = 100,
Regularization = 1e-6
};
// Use the teacher algorithm to learn the regression:
LogisticRegression lr = teacher.Learn(inputs, outputs);
// Classify the samples using the model
bool[] answers = lr.Decide(inputs);
// Convert to Int32 so we can plot:
int[] zeroOneAnswers = answers.ToZeroOne();
// Plot the results
ScatterplotBox.Show("Expected results", inputs, outputs);
ScatterplotBox.Show("Logistic Regression results", inputs, zeroOneAnswers)
.Hold();
See Logistic Regression.
In some problems, samples can belong to more than one single class at a time. Those problems are denoted multiple label classification problems and can be solved in different manners. One way to attack a multi-label problem is by using a 1-vs-all support vector machine.
// The following is simple auto association function where each input correspond
// to its own class. This problem should be easily solved by a Linear kernel.
// Sample input data
double[][] inputs =
{
new double[] { 0 },
new double[] { 3 },
new double[] { 1 },
new double[] { 2 },
};
// Outputs for each of the inputs
int[][] outputs =
{
new[] { -1, 1, -1 },
new[] { -1, -1, 1 },
new[] { 1, 1, -1 },
new[] { -1, -1, -1 },
};
// Create the Multi-label learning algorithm for the machine
var teacher = new MultilabelSupportVectorLearning<Linear>()
{
Learner = (p) => new SequentialMinimalOptimization<Linear>()
{
Complexity = 10000.0 // Create a hard SVM
}
};
// Learn a multi-label SVM using the teacher
var svm = teacher.Learn(inputs, outputs);
// Compute the machine answers for the inputs
bool[][] answers = svm.Decide(inputs);
// Use the machine as if it were a multi-class machine
// instead of a multi-label, identifying the strongest
// class among the multi-label predictions:
int[] maxAnswers = svm.ToMulticlass().Decide(inputs);See Multi-label SVM.
A sequence classification problem is a classification problem where input vectors can have varying length. Those problems can be attacked in multiple ways. One of them is to use a classifier that has been specifically designed to work with sequences. The other one is to extract a fixed number of features from those varying length vectors, and then use them with any standard classification algorithms, such as support vector machines.
For an example on how to transform sequences into fixed length vectors, see Dynamic Time Warp Support Vector Machine.
// Declare some training data
int[][] inputs = new int[][]
{
new int[] { 0,1,1,0 }, // Class 0
new int[] { 0,0,1,0 }, // Class 0
new int[] { 0,1,1,1,0 }, // Class 0
new int[] { 0,1,0 }, // Class 0
new int[] { 1,0,0,1 }, // Class 1
new int[] { 1,1,0,1 }, // Class 1
new int[] { 1,0,0,0,1 }, // Class 1
new int[] { 1,0,1 }, // Class 1
};
int[] outputs = new int[]
{
0,0,0,0, // First four sequences are of class 0
1,1,1,1, // Last four sequences are of class 1
};
// We are trying to predict two different classes
int classes = 2;
// Each sequence may have up to two symbols (0 or 1)
int symbols = 2;
// Nested models will have two states each
int[] states = new int[] { 2, 2 };
// Creates a new Hidden Markov Model Classifier with the given parameters
HiddenMarkovClassifier classifier = new HiddenMarkovClassifier(classes, states, symbols);
// Create a new learning algorithm to train the sequence classifier
var teacher = new HiddenMarkovClassifierLearning(classifier,
// Train each model until the log-likelihood changes less than 0.001
modelIndex => new BaumWelchLearning(classifier.Models[modelIndex])
{
Tolerance = 0.001,
MaxIterations = 0
}
);
// Train the sequence classifier using the algorithm
teacher.Learn(inputs, outputs);
// Compute the classifier answers for the given inputs
int[] answers = classifier.Decide(inputs);For examples of sequence classifiers, see Hidden Markov Classifier Learning and Hidden Conditional Random Field Learning.
// Suppose we would like to learn how to classify the
// following set of sequences among three class labels:
int[][] inputs =
{
// First class of sequences: starts and
// ends with zeros, ones in the middle:
new[] { 0, 1, 1, 1, 0 },
new[] { 0, 0, 1, 1, 0, 0 },
new[] { 0, 1, 1, 1, 1, 0 },
// Second class of sequences: starts with
// twos and switches to ones until the end.
new[] { 2, 2, 2, 2, 1, 1, 1, 1, 1 },
new[] { 2, 2, 1, 2, 1, 1, 1, 1, 1 },
new[] { 2, 2, 2, 2, 2, 1, 1, 1, 1 },
// Third class of sequences: can start
// with any symbols, but ends with three.
new[] { 0, 0, 1, 1, 3, 3, 3, 3 },
new[] { 0, 0, 0, 3, 3, 3, 3 },
new[] { 1, 0, 1, 2, 2, 2, 3, 3 },
new[] { 1, 1, 2, 3, 3, 3, 3 },
new[] { 0, 0, 1, 1, 3, 3, 3, 3 },
new[] { 2, 2, 0, 3, 3, 3, 3 },
new[] { 1, 0, 1, 2, 3, 3, 3, 3 },
new[] { 1, 1, 2, 3, 3, 3, 3 },
};
// Now consider their respective class labels
int[] outputs =
{
/* Sequences 1-3 are from class 0: */ 0, 0, 0,
/* Sequences 4-6 are from class 1: */ 1, 1, 1,
/* Sequences 7-14 are from class 2: */ 2, 2, 2, 2, 2, 2, 2, 2
};
// Create the Hidden Conditional Random Field using a set of discrete features
var function = new MarkovDiscreteFunction(states: 3, symbols: 4, outputClasses: 3);
var classifier = new HiddenConditionalRandomField<int>(function);
// Create a learning algorithm
var teacher = new HiddenResilientGradientLearning<int>(classifier)
{
MaxIterations = 50
};
// Run the algorithm and learn the models
teacher.Learn(inputs, outputs);
// Compute the classifier answers for the given inputs
int[] answers = classifier.Decide(inputs);