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What is Machine Learning with Java? How to Implement it?

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10th Mar, 2021
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What is Machine Learning with Java? How to Implement it?

What is Machine Learning? 

Machine learning is a division of Artificial Intelligence that learns from available data, examples, and experiences to mimic human behaviour and intelligence. A program created using machine learning can build logic on its own without a human having to manually write the code. 

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It all began with The Turing Test in the early 1950s when Alan Turning concluded that for a computer to have real intelligence, it would need to manipulate or convince a human that it was human too. Machine learning is a relatively old concept but it’s only today that this emerging field is subject to realization since computers now can process complex algorithms. Machine learning algorithms have evolved over the past decade to include complex computational skills which in turn has led to an enhancement in their mimicking capabilities. 

Machine learning applications have also increased at an alarming rate. From healthcare, finance, analytics, and education, to manufacturing, marketing, and government operations, every industry has seen a significant boost in quality and efficiency after implementing machine learning technologies. There have been widespread qualitative improvements all over the world, hence, driving the demand for machine learning professionals.

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Types of Machine Learning Algorithms

Machine learning algorithms are of three types:

1. Supervised Learning: In this type of learning, training data sets guide an algorithm to making accurate predictions or analytical decisions. It employs learning from past training datasets to process new data. Here are a few examples of supervised learning machine learning models:

  1. Linear regression
  2. Logistic regression
  3. Decision tree

2. Unsupervised Learning: In this type of learning, a machine learning model learns from unlabeled pieces of information. It employs data clustering by grouping objects or understanding the relationship between them, or exploiting their statistical properties to conduct analysis. Examples of unsupervised learning algorithms are:

    1. K-means clustering
    2. Hierarchical clustering

3. Reinforcement Learning: This process is based on hit and trial. It is learning by interacting with space or an environment. An RL algorithm learns from its past experiences by interacting with the environment and determining the best course of action. 

How to Implement Machine Learning with Java?

Java is among the top programming languages used for implementing machine learning algorithms. Most of its libraries are open-source, providing extensive documentation support, easy maintenance, marketability, and easy readability. 

Depending on the popularity, here are the top 10 machine learning libraries used to implement machine learning in Java. 

1. ADAMS

Advanced-Data mining And Machine learning System or ADAMS is concerned with building a novel and flexible workflow systems and to manage complex real-world processes. ADAMS employs a tree-like architecture to manage data flow instead of making manual input-output connections.

It eliminates any need for explicit connections. It is based on the “less is more” principle and performs retrieval, visualization, and data-driven visualizations. ADAMS is adept at data processing, data streaming, managing databases, scripting, and documentation.

2. JavaML

JavaML offers a variety of ML and data mining algorithms that are written for Java to support software engineers, programmers, data scientists, and researchers. Every algorithm has a common interface that is easy to use and has extensive documentation support even though there is no GUI.

It is rather simple and straightforward to implement in comparison with other clustering algorithms. Its core features include data manipulation, documentation, database management, data classification, clustering, feature selection, and so on.

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3. WEKA

Weka is also an open-source machine learning library written for Java that supports deep learning. It provides a set of machine learning algorithms and finds extensive use in data mining, data preparation, data clustering, data visualization, and regression, among other data operations.

Example: We will demonstrate this using a small diabetes dataset. 

Step 1: Load the data using Weka

import weka.core.Instances;

import weka.core.converters.ConverterUtils.DataSource;

public class Main {

    public static void main(String[] args) throws Exception {

        // Specifying the datasource

        DataSource dataSource = new DataSource(“data.arff”);

        // Loading the dataset

        Instances dataInstances = dataSource.getDataSet();

        // Displaying the number of instances

        log.info(“The number of loaded instances is: ” + dataInstances.numInstances());

        log.info(“data:” + dataInstances.toString());

    }

}

Step 2: The dataset has 768 instances. We need to access the number of attributes, i.e., 9.

log.info(“The number of attributes (features) in the dataset: ” + dataInstances.numAttributes());

Step 3: We need to determine the target column before we build a model and find the number of classes. 

// Identifying the label index

dataInstances.setClassIndex(dataInstances.numAttributes() – 1);

// Getting the number of 

log.info(“The number of classes: ” + dataInstances.numClasses());

Step 4: We will now build the model using a simple tree classifier, J48.

// Creating a decision tree classifier

J48 treeClassifier = new J48();

treeClassifier.setOptions(new String[] { “-U” });

treeClassifier.buildClassifier(dataInstances);

The code above highlights how to create an unpruned tree that consists of the data instances required for model training. Once the tree structure is printed after the model training, we can determine how the rules were built internally. 

plas <= 127

|   mass <= 26.4

|   |   preg <= 7: tested_negative (117.0/1.0)

|   |   preg > 7

|   |   |   mass <= 0: tested_positive (2.0)

|   |   |   mass > 0: tested_negative (13.0)

|   mass > 26.4

|   |   age <= 28: tested_negative (180.0/22.0)

|   |   age > 28

|   |   |   plas <= 99: tested_negative (55.0/10.0)

|   |   |   plas > 99

|   |   |   |   pedi <= 0.56: tested_negative (84.0/34.0)

|   |   |   |   pedi > 0.56

|   |   |   |   |   preg <= 6

|   |   |   |   |   |   age <= 30: tested_positive (4.0)

|   |   |   |   |   |   age > 30

|   |   |   |   |   |   |   age <= 34: tested_negative (7.0/1.0)

|   |   |   |   |   |   |   age > 34

|   |   |   |   |   |   |   |   mass <= 33.1: tested_positive (6.0)

|   |   |   |   |   |   |   |   mass > 33.1: tested_negative (4.0/1.0)

|   |   |   |   |   preg > 6: tested_positive (13.0)

plas > 127

|   mass <= 29.9

|   |   plas <= 145: tested_negative (41.0/6.0)

|   |   plas > 145

|   |   |   age <= 25: tested_negative (4.0)

|   |   |   age > 25

|   |   |   |   age <= 61

|   |   |   |   |   mass <= 27.1: tested_positive (12.0/1.0)

|   |   |   |   |   mass > 27.1

|   |   |   |   |   |   pres <= 82

|   |   |   |   |   |   |   pedi <= 0.396: tested_positive (8.0/1.0)

|   |   |   |   |   |   |   pedi > 0.396: tested_negative (3.0)

|   |   |   |   |   |   pres > 82: tested_negative (4.0)

|   |   |   |   age > 61: tested_negative (4.0)

|   mass > 29.9

|   |   plas <= 157

|   |   |   pres <= 61: tested_positive (15.0/1.0)

|   |   |   pres > 61

|   |   |   |   age <= 30: tested_negative (40.0/13.0)

|   |   |   |   age > 30: tested_positive (60.0/17.0)

|   |   plas > 157: tested_positive (92.0/12.0)

Number of Leaves  :  22

Size of the tree :  43

4. Apache Mahaut

Mahaut is a collection of algorithms to help implement machine learning using Java. It is a scalable linear algebra framework using which developers can carry out mathematics, statisticians analytics. It is usually used by data scientists, research engineers, and analytics professionals to build enterprise-ready applications. Its scalability and flexibility allows users to implement data clustering, recommendation systems, and create performant Machine learning apps quickly and easily.

5. Deeplearning4j

Deeplearning4j is a programming library that is written in Java and offers extensive support for deep learning. It is an open-source framework that combines deep neural networks and deep reinforcement learning to serve business operations. It is compatible with Scala, Kotlin, Apache Spark, Hadoop, and other JVM languages and big data computing frameworks.

It is typically used to detect patterns and emotions in voice, speech, and written text. It serves as a DIY tool that can discover discrepancies in transactions, and handle multiple tasks. It is a commercial-grade, distributed library that has detailed API documentation owing to its open-sourced nature. 

Here is an example of how you can implement machine learning using Deeplearning4j. 

Example: Using Deeplearning4j, we will build a Convolution Neural Network (CNN) model to classify the handwritten digits with the help of the MNIST library.

Step 1: Load the dataset to display its size.

DataSetIterator MNISTTrain = new MnistDataSetIterator(batchSize,true,seed);

DataSetIterator MNISTTest = new MnistDataSetIterator(batchSize,false,seed);

Step 2: Ensure that the dataset gives us ten unique labels.

log.info(“The number of total labels found in the training dataset ” + MNISTTrain.totalOutcomes());

log.info(“The number of total labels found in the test dataset ” + MNISTTest.totalOutcomes());

Step 3: Now, we will configure the model architecture using two convolution layers along with a flattened layer to display the output. 

There are options in Deeplearning4j which allow you to initialize the weight scheme.

// Building the CNN model

MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()

        .seed(seed) // random seed

        .l2(0.0005) // regularization

        .weightInit(WeightInit.XAVIER) // initialization of the weight scheme

        .updater(new Adam(1e-3)) // Setting the optimization algorithm

        .list()

        .layer(new ConvolutionLayer.Builder(5, 5)

                //Setting the stride, the kernel size, and the activation function.

                .nIn(nChannels)

                .stride(1,1)

                .nOut(20)

                .activation(Activation.IDENTITY)

                .build())

        .layer(new SubsamplingLayer.Builder(PoolingType.MAX) // downsampling the convolution

                .kernelSize(2,2)

                .stride(2,2)

                .build())

        .layer(new ConvolutionLayer.Builder(5, 5)

                // Setting the stride, kernel size, and the activation function.

                .stride(1,1)

                .nOut(50)

                .activation(Activation.IDENTITY)

                .build())

        .layer(new SubsamplingLayer.Builder(PoolingType.MAX) // downsampling the convolution

                .kernelSize(2,2)

                .stride(2,2)

                .build())

        .layer(new DenseLayer.Builder().activation(Activation.RELU)

                .nOut(500).build())

        .layer(new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)

                .nOut(outputNum)

                .activation(Activation.SOFTMAX)

                .build())

        // the final output layer is 28×28 with a depth of 1.

        .setInputType(InputType.convolutionalFlat(28,28,1))

        .build();

Step 4: After we have configured the architecture, we will initialize the mode and the training dataset, and begin the model training.

MultiLayerNetwork model = new MultiLayerNetwork(conf);

// initialize the model weights.

model.init();

log.info(“Step2: start training the model”);

//Setting a listener every 10 iterations and evaluate on test set on every epoch

model.setListeners(new ScoreIterationListener(10), new EvaluativeListener(MNISTTest, 1, InvocationType.EPOCH_END));

// Training the model

model.fit(MNISTTrain, nEpochs);

As the model training commences, you will have the confusion matrix of the classification accuracy.

Here’s the accuracy of the model after ten training  epochs:

=========================Confusion Matrix=========================

    0    1    2    3    4    5    6    7    8    9

—————————————————

  977    0    0    0    0    0    1    1    1    0 | 0 = 0

    0 1131    0    1    0    1    2    0    0    0 | 1 = 1

    1    2 1019    3    0    0    0    3    4    0 | 2 = 2

    0    0    1 1004    0    1    0    1    3    0 | 3 = 3

    0    0    0    0  977    0    2    0    1    2 | 4 = 4

    1    0    0    9    0  879    1    0    1    1 | 5 = 5

    4    2    0    0    1    1  949    0    1    0 | 6 = 6

    0    4    2    1    1    0    0 1018    1    1 | 7 = 7

    2    0    3    1    0    1    1    2  962    2 | 8 = 8

    0    2    0    2   11    2    0    3    2  987 | 9 = 9

6. ELKI

Environment for Developing KDD-Applications Supported by Index-structure or ELKI is a collection of built-in algorithms and programs used for data mining. Written in Java, it is an open-source library that comprises highly configurable parameters in algorithms. It is typically used by research scientists and students to gain insights into datasets. As the name suggests, it provides an environment for developing sophisticated data mining programs and databases using an index-structure. 

7. JSAT

Java Statistical Analysis Tool or JSAT is a GPL3 library that uses an object-oriented framework to help users implement machine learning with Java. It’s typically used for self-education purposes by students and developers. As compared to other AI implementation libraries, JSAT has the highest number of ML algorithms and is the fastest amongst all frameworks. With zero external dependencies, it is highly flexible and efficient and offers high performance. 

8. The Encog Machine Learning Framework

Encog is written in Java and C# and comprises libraries that help implement machine learning algorithms. It is used for building genetic algorithms, Bayesian Networks, statistical models like the Hidden Markov Model, and more. 

9. Mallet

Machine Learning for Language Toolkit or Mallet is used in Natural Language Processing (NLP). Like most other ML implementation frameworks, Mallet also provides support for data modelling, data clustering, document processing, document classification, and so on.

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10. Spark MLlib

Spark MLlib is used by businesses to enhance the efficiency and scalability of workflow management. It processes copious amounts of data and supports heavy-loaded ML algorithms. 

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Conclusion

This brings us to the end of the article. For more information on Machine Learning concepts, get in touch with the top faculty of IIIT Bangalore and Liverpool John Moores University through upGrad‘s Master of Science in Machine Learning & AI program.

Profile

Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
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Frequently Asked Questions (FAQs)

1Why should we use Java along with Machine Learning?

Machine Learning professionals will find it easier to interface with current code repositories if they choose Java as the programming language for their projects. It is a Machine Learning language of preference due to features like ease of use, package services, better user interaction, rapid debugging, and graphical illustration of data. Java makes it easy for Machine Learning developers to scale their systems, making it an excellent choice for building big, sophisticated Machine Learning applications from the ground up. Java Virtual Machine (JVM) supports a number of integrated development environments (IDEs) that allow machine learners to design new tools quickly.

2Is learning Java easy?

As Java is a high-level language, it is simple to grasp. As a learner, you won't have to go into as much detail as it is a well-structured, object-oriented language that's simple enough for novices to pick up. Because there are numerous procedures that operate automatically, you can master them quickly. You don't have to go into great detail on how things operate in there. Java is a platform-independent programming language. It enables a programmer to create a mobile application that can be used on any device. It's the Internet of Things' preferred language, as well as the best tool for developing enterprise-level applications.

3What is ADAMS, and how is it helpful in Machine Learning?

The Advanced Data Mining And Machine Learning System (ADAMS) is a GPLv3-licensed workflow engine for fast creating and managing data-driven, reactive workflows that may be readily incorporated into business processes. The workflow engine, which follows the principle of less is more, lies at the heart of ADAMS. ADAMS employs a tree-like structure instead of allowing the user to arrange operators (or actors in ADAMS jargon) on a canvas and then manually link inputs and outputs. No explicit connections are required because this structure and the control actors determine how the data flows in the process. The internal object representation and sub-operator nesting within operator handlers result in a tree-like structure. ADAMS provides a diverse set of agents for data retrieval, processing, mining, and display.

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