In the past decade, machine learning has become one of the most popular AI fields along with natural language processing and deep learning. Tech giants have come up with various applications ranging from online advertising targeting to self-driving cars. Consequently, the tools and libraries for machine learning that simplify the development process is also growing.
Reflecting on the popularity of Java, some exceptional tools have also been introduced for implementing machine learning (ML) with Java. Due to its extreme stability and a giant user base, leading organizations and enterprises have been adopting Java for decades. It has been widely used in mobile app development for Android which serves billions of users around the globe.
This article features the top ten libraries and tools to implement machine learning with Java, it further explains the salient features of each tool and the ML algorithms they support.
First on our list is Java Machine Learning Library or Java-ML. It is an open-source Java API targeting software engineers, programmers, and computer scientists who want to work in machine learning with Java. It offers a vast collection of Java machine learning and data mining algorithms including algorithms for data preprocessing, feature selection, classification, and clustering. It provides a straightforward easy-to-implement clustering algorithm as compared to other frameworks. You do not get a GUI with Java-ML, but it offers a clear and very common interface to work with.
Java-ML has a well-documented source code and plenty of code samples and tutorials, which makes it a great option, especially for those who are new to implementing machine learning with Java.
2. Apache Spark’s MLib
Apache Spark is a platform for large-scale data processing. It is built on top of Hadoop. It comes with MLib, a scalable machine learning library written in Scala. MLib can be easily used with Java along with Python, R, and Scala. It supports algorithms for classification, regression, collaborative filtering, clustering, dimensionality reduction, and optimization.
3. Deep Learning for Java (Deeplearning4j)
If you are more focused on deep learning and are willing to explore it with Java, you have a great option for that. Deeplearning4j or DL4J for short is the first official commercial-grade, open-source distributed deep Java learning library. It is also fully compatible with various other JVM languages, like Scala, Clojure, and Kotlin.
It was primarily designed to make deep neural networks and deep reinforcement learning to be more applicable for business and other applications rather than just for research. DL4J provides APIs for neural network creation. Deep neural networks and deep reinforcement learning are now capable of pattern recognition and goal-oriented ML which makes DL4J extremely useful for identifying patterns and sentiment in sound, text, and even speech by detecting abnormalities in financial transactions, and by identifying spam or e-commerce fraud.
ELKI (Environment for Developing KDD-Applications Supported by Index Structures) is an open-source data mining software fully written in Java. It is primarily designed for researchers and students aiming to create sensible and more stable databases. It provides a variety of extremely configurable algorithms for data mining as well as data management. ELKI also offers R*-tree and other data index structures for better performance and scalability.
The Java Statistical Analysis Tool or JSAT has one of the largest collections of Java machine learning algorithms. It is written in pure Java and has no external dependencies. The only downside here is that a major part of the library was intended for self-education and not for implementation thus all of the source code is self-contained. It makes this more suitable for only small and medium-size problems.
KNIME, or the Konstanz Information Miner, is a free and open-source data analytics, reporting, and integration tool. The highlighting point of KNIME is the integration of various components for machine learning and data mining through its modular data pipelining concept, called “Lego of Analytics”. It offers a user-friendly graphical user interface which makes it very easy to learn. It can also be used for business intelligence, financial data analysis, and CRM and can work as a SAS alternative.
Despite all these features, it is still difficult to build complicated models due to limited visualization and exporting capabilities.
Apache Mahout is a distributed linear algebra framework and mathematically expressive Scala DSL. It is designed to target mathematicians, statisticians, and data scientists for executing their algorithms. Mahout is written in Java and Scala and its built-in machine learning algorithms facilitate easier and faster implementation of machine learning with Java. Mahout is built on top of scalable distributed architectures. It features a console-based interface and Java APIs to scalable algorithms for clustering, classification, and collaborative filtering.
Mahout is business-ready for machine learning implementations and proves to be extremely useful for solving these three types of problems:
- item recommendation, for instance, in a recommendation system;
- clustering, to create groups of topically related documents;
- classification, for learning the topics to be assigned to an unlabeled document.
MOA, or Massive Online Analysis, is one of the most popular open-source frameworks for data stream mining. It is specifically used for machine learning and data mining on data streams in real-time. Its Java machine learning algorithms and tools for evaluation are very useful for classification, regression, clustering, outlier detection, concept drift detection, and recommendation systems. MOA is most suited to be used with large evolving datasets and data streams, as well as real-time data produced by IoT devices.
MOA provides a benchmark framework for running various experiments on data in the data mining field. Some of its highlighting features include:
- The framework is extendable for adding new mining algorithms, new steam generators, and evaluation measures;
- The settings for data streams can be stored for repeatable experiments;
RapidMiner is one of the most advanced machine learning tools among all. It is a commercial data science platform that was built for analytics and research. It is presently powering some renowned tech giants like Cisco, Samsung, Hitachi, Salesforce, GE, Siemens, and various other companies.
RapidMiner offers a set of features and tools to simplify the processes for building new data mining processes, to set up predictive analysis, and numerous other processes that initially can only be performed by scientists. This data science platform includes a variety of machine learning libraries and algorithms through GUI and Java API for developing your applications. These ML algorithms can be used for the seamless implementation of machine learning with Java in your applications.
RapidMiner also has a big community of users and offers extensive documentation. Data scientists can leverage features like selection, data loading, and cleaning. By using GUI, visual workflows can also be created.
The only downside over here is that this tool is costly. Although there is a free plan option, it offers very limited features making it not ideal especially for large-scale work.
Last but not least, Weka is arguably one of the most well-known and popular libraries among all for implementing machine learning with Java. Being an open-source library, it is free for its users. This general-purpose library offers a rich and user-friendly graphical user interface along with a command-line interface as well as various Java APIs, for implementing algorithms. The machine learning algorithms can be directly applied to the dataset, either through the provided GUI or by calling from your Java code through the provided Java API.
It provides a set of tools for data preparation, classification, regression, clustering, association rules mining, time series prediction, feature selection, anomaly detection, as well as for visualization. Weka also offers some advanced features for setting up long-running mining sessions, experimentations and comparing various Machine learning algorithms. It also allows developers to run machine learning algorithms on text files. Weka is primarily used for data mining, data analysis, and predictive modeling. It is an ideal option for applications that require the automatic classification of data. It is also well-suited for developing new Machine Learning schemes and implementing machine learning with Java.
These are some exceptional Java platforms and libraries to implement machine learning with Java. While some are free being open source libraries, there are quite a few paid tools that are robust and can be your best option if you are willing to spend some money. All of these tools have a unique approach towards machine learning. Undoubtedly, the implementation of machine learning is gaining momentum in almost all applications and it is high time that Java developers planning to explore machine learning can try out these tools to implement it with Java.