Java machine learning

The Java Machine Learning Library (Java-ML) is a collection of machine learning algorithms with a common interface for algorithms of the same type. It only features the Java API, and so it is primarily aimed at software engineers and programmers. Java-ML contains algorithms for data preprocessing, feature selection, classification, and clustering. In addition, it features several Weka bridges to access Weka's algorithms directly through the Java-ML API. It can be downloaded from http://java-ml.sourceforge.net.

Java-ML is also a general-purpose machine learning library. Compared to Weka, it offers more consistent interfaces and implementations of recent algorithms that are not present in other packages, such as an extensive set of state-of-the-art similarity measures and feature-selection techniques, for example, dynamic time warping (DTW), random forest attribute evaluation, and so on. Java-ML is also available under the GNU GPL license.

Java-ML supports all types of files as long as they contain one data sample per line and the features are separated by a symbol such as a comma, semicolon, or tab.

The library is organized around the following top-level packages:

• net.sf.javaml.classification: These are classification algorithms, including Naive Bayes, random forests, bagging, self-organizing maps, k-nearest neighbors, and so on
• net.sf.javaml.clustering: These are clustering algorithms such as k-means, self-organizing maps, spatial clustering, Cobweb, ABC, and others
• net.sf.javaml.core: These are classes representing instances and datasets
• net.sf.javaml.distance: These are algorithms that measure instance distance and similarity, for example, Chebyshev distance, cosine distance/similarity, Euclidean distance, Jaccard distance/similarity, Mahalanobis distance, Manhattan distance, Minkowski distance, Pearson correlation coefficient, Spearman's footrule distance, DTW, and so on
• net.sf.javaml.featureselection: These are algorithms for feature evaluation, scoring, selection, and ranking, for instance, gain ratio, ReliefF, Kullback-Leibler divergence, symmetrical uncertainty, and so on
• net.sf.javaml.filter: These are methods for manipulating instances by filtering, removing attributes, setting classes or attribute values, and so on
• net.sf.javaml.matrix: This implements in-memory or file-based arrays
• net.sf.javaml.sampling: This implements sampling algorithms to select a subset of datasets
• net.sf.javaml.tools: These are utility methods on dataset, instance manipulation, serialization, Weka API interface, and so on
• net.sf.javaml.utils: These are utility methods for algorithms, for example, statistics, math methods, contingency tables, and others