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Machine Learning in Java

You're reading from   Machine Learning in Java Helpful techniques to design, build, and deploy powerful machine learning applications in Java

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788474399
Length 300 pages
Edition 2nd Edition
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Authors (2):
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Ashish Bhatia Ashish Bhatia
Author Profile Icon Ashish Bhatia
Ashish Bhatia
Bostjan Kaluza Bostjan Kaluza
Author Profile Icon Bostjan Kaluza
Bostjan Kaluza
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Table of Contents (13) Chapters Close

Preface 1. Applied Machine Learning Quick Start FREE CHAPTER 2. Java Libraries and Platforms for Machine Learning 3. Basic Algorithms - Classification, Regression, and Clustering 4. Customer Relationship Prediction with Ensembles 5. Affinity Analysis 6. Recommendation Engines with Apache Mahout 7. Fraud and Anomaly Detection 8. Image Recognition with Deeplearning4j 9. Activity Recognition with Mobile Phone Sensors 10. Text Mining with Mallet - Topic Modeling and Spam Detection 11. What Is Next? 12. Other Books You May Enjoy

Fraud and Anomaly Detection

Outlier detection is used to identify exceptions, rare events, and other anomalous situations. Such anomalies may be needles in a haystack, but their consequences can nonetheless be quite dramatic; for instance, credit card fraud detection, identifying network intrusions, faults in manufacturing processes, clinical trials, voting activities, and criminal activities in e-commerce. Therefore, anomalies represent a high value when they are found and high costs if they are not. Applying machine learning to outlier detection problems can bring new insights and better detection of outlier events. Machine learning can take into account many disparate sources of data, and can find correlations that are too obscure for human analysis to identify.

Take the example of e-commerce fraud detection. With machine learning algorithms in place, the purchaser's online...

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