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25 Java Machine Learning Tools & Libraries

This article about java machine learning tools & libraries comes from BDAN Big Data Analytics News. BDAN’s site consists information on business trends, big data use cases, big data news to help you learn what Big Data is and how it can benefit organizations of all sizes.


Here is a list of 10 Java Machine learning tools & libraries:

  1. Weka has a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
  2. Massive Online Analysis (MOA) is a popular open source framework for data stream mining, with a very active growing community. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation. Related to the WEKA project, MOA is also written in Java, while scaling to more demanding problems.
  3. The MEKA project provides an open source implementation of methods for multi-label learning and evaluation. In multi-label classification, we want to predict multiple output variables for each input instance. This different from the ‘standard’ case which involves only a single target variable. MEKA is based on the WEKA Machine Learning Toolkit.
  4. The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows, released under GPLv3.
  5. Environment for Developing KDD-Applications Supported by Index-Stru… (ELKI) is an open source (AGPLv3) data mining software written in Java. The focus of ELKI is research in algorithms, with an emphasis on unsupervised methods in cluster analysis and outlier detection.
  6. Mallet is a java machine learning toolkit for  textual document. Mallet supports classification algorithms like maximum entropy, naive bayes and decision tree for classification.
  7. Encog is an advanced machine learning framework which supports Support Vector Machines,Artificial Neural Networks, Genetic Programming, Bayesian Networks, Hidden Markov Models, Genetic Programming and Genetic Algorithms are supported.
  8. The Datumbox Machine Learning Framework is an open-source framework written in Java which allows the rapid development Machine Learning and Statistical applications. The main focus of the framework is to include a large number of machine learning algorithms & statistical tests and being able to handle medium-large sized datasets.
  9. Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. It is designed to be used in business environments, rather than as a research tool.
  10. Mahout is a machine learning framework with built in algorithms. Mahout-Samsara helps people create their own math while providing some off-the-shelf algorithm implementations.

To read the original article and see the 15 other Java Machine learning tools & libraries, click here. For more articles about Java Machine Learning, click here

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