Once sensor samples are represented as feature vectors and have the class assigned, it is possible to apply standard techniques for supervised classification, including feature selection, feature discretization, model learning, k-fold cross-validation, and so on. The chapter will not delve into the details of the machine learning algorithms. Any algorithm that supports numerical features can be applied, including SVMs, random forest, AdaBoost, decision trees, neural networks, multilayer perceptrons, and others.
Therefore, let's start with a basic one: decision trees. Here, we will load the dataset, build the set class attribute, build a decision tree model, and output the model:
String databasePath = "/Users/bostjan/Dropbox/ML Java Book/book/datasets/chap9/features.arff"; // Load the data in arff format Instances data = new Instances(new...
