WEKA: Practical Machine Learning Tools and Techniques with Java Implementations

Abstract

With recent advances in computer technology large amounts of data could be collected and stored. But all this data becomes more useful when it is analyzed and some dependencies and correlations are detected. This can be accomplished with machine learning algorithms. WEKA (Waikato Environment for Knowledge Analysis) is a collection of machine learning algorithms implemented in Java. WEKA consists of a large number of learning schemes for classification and regression numeric prediction like decision trees, support vector machines, instance-based classifiers, Bayes decision schemes, neural networks etc. and clustering. It provides also meta classifiers like bagging and boosting, evaluation methods like cross-validation and bootstrapping, numerous attribute selection methods and preprocessing techniques. A graphical user interface provides loading of data, applying machine learning algorithms and visualizing the built models. A Java interface available to all algorithms enables embedding them in any user’s program.

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