Anomaly Detection in Machine Learning: Finding Outliers to Optimize Business Functions

As organizations collect large data sets with potential insights into business activities, detecting unusual data or outliers in these data sets is essential to discover inefficiencies, rare events, root causes of problems, or opportunities for operational improvement. no see. So what are anomalies and why is it important to detect them?

The types of anomalies vary depending on the enterprise and business function. Anomaly detection simply means defining “normal” patterns and indicators based on business functions and goals and identifying data points that fall outside the normal behavior of your operations. For example, higher-than-average traffic to a website or application over a certain period of time could indicate a cybersecurity threat, in which case you would want a system that can automatically trigger fraud detection alerts. This could also be a sign that a particular marketing plan is working. Anomalies are not inherently bad, but recognizing them and having data in context is essential to understanding and protecting your business.

The challenge for IT departments working in data science is to make sense of expanding and constantly changing data points. In this blog, we will look at how to leverage machine learning techniques powered by artificial intelligence to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection, and semi-supervised anomaly detection.

supervised learning

Supervised learning techniques use real input and output data to detect anomalies. This type of anomaly detection system requires data analysts to label data points as normal or abnormal to use as training data. A machine learning model trained with labeled data can detect outliers based on the examples provided. This type of machine learning is useful for detecting known outliers, but it cannot discover unknown outliers or predict future problems.

Common machine learning algorithms for supervised learning include:

• KNN (K-Nearest Neighbor) Algorithm: This algorithm is a density-based classifier or regression modeling tool used for anomaly detection. Regression modeling is a statistical tool used to find relationships between labeled and variable data. It works by assuming that similar data points are found near each other. If a data point appears further away from a dense section of points, this is considered an anomaly.
• Local Outlier Factor (LOF)): Local Outlier Factor is similar to KNN in that it is a density-based algorithm. The key difference is that KNN makes assumptions based on the closest data points, while LOF uses the most distant points to draw conclusions.

unsupervised learning

Unsupervised learning techniques do not require labeled data and can handle more complex data sets. Unsupervised learning is powered by deep learning and neural networks, or autoencoders that mimic the way biological neurons signal to each other. These powerful tools can find patterns in input data and make assumptions about what data is recognized as normal.

These techniques can be of great help in discovering unknown anomalies and reducing the work of manually sifting through large data sets. However, data scientists must monitor the results collected through unsupervised learning. Because these techniques make assumptions about the data being input, they can potentially mislabel anomalies.

Machine learning algorithms for unstructured data include:

K-means: This algorithm is a data visualization technique that processes data points through mathematical equations with the intention of clustering similar data points. The “average” or mean data represents the point at which the cluster is centered relative to all other data. Through data analysis, you can use these clusters to find patterns and make inferences about data that turns out to be out of the ordinary.

Quarantine Forest: These types of anomaly detection algorithms use unsupervised data. Unlike supervised anomaly detection techniques that operate on labeled normal data points, this technique attempts to isolate anomalies as a first step. Similar to a “random forest,” we create a “decision tree” that maps data points and randomly selects areas for analysis. This process is repeated and each point is given an anomaly score between 0 and 1 based on its position relative to other points. Values ​​below 0.5 are generally considered normal, while values ​​above that threshold are more likely to be abnormal. The isolated forest model can be found in scikit-learn, a free machine learning library for Python.

Single-class support vector machine (SVM): This anomaly detection technique uses training data to create boundaries around what is considered normal. Clustered points within the set boundaries are considered normal and points outside are marked as anomalies.

semi-supervised learning

Semi-supervised anomaly detection methods combine the advantages of the previous two methods. Engineers can apply unsupervised learning methods to automate feature learning and work with unstructured data. But by combining this with human supervision, you have the opportunity to monitor and control what kinds of patterns your model is learning. This generally helps make the model’s predictions more accurate.

Linear Regression: This predictive machine learning tool uses both dependent and independent variables. The independent variable is used as the basis for determining the value of the dependent variable through a series of statistical equations. These equations use labeled and unlabeled data to predict future outcomes when only part of the information is known.

Anomaly detection use cases

Anomaly detection is a critical tool for maintaining business functionality across a variety of industries. The use of supervised, unsupervised, and semi-supervised learning algorithms depends on the type of data being collected and the operational problem being solved. Examples of anomaly detection use cases include:

Supervised learning use cases:

Sleeve

Using labeled data from previous year’s total sales can help you predict future sales goals. It can also help you set benchmarks for specific sales staff based on past performance and overall company needs. Since all your sales data is known, you can analyze patterns to gain insights about your products, marketing, and seasonality.

weather forecast

Supervised learning algorithms can help predict weather patterns using historical data. Analyzing recent data related to barometric pressure, temperature and wind speed allows meteorologists to create more accurate forecasts that take changing conditions into account.

Unsupervised learning use cases:

Intrusion detection system

These types of systems come in the form of software or hardware that monitors network traffic for signs of security breaches or malicious activity. Machine learning algorithms can be trained to detect potential attacks on your network in real time and protect user information and system functions.

These algorithms visualize normal performance based on time series data, allowing data points to be analyzed at set intervals over long periods of time. Spikes or unexpected patterns in network traffic can be flagged and inspected as potential security violations.

Operation

Ensuring your machines are operating properly is critical to manufacturing products, optimizing quality assurance, and maintaining your supply chain. Unsupervised learning algorithms can be used for predictive maintenance by taking unlabeled data from sensors attached to equipment and making predictions about potential errors or malfunctions. This allows companies to reduce machine downtime by performing repairs before serious failures occur.

Semi-supervised learning use cases:

Medical treatment

Medical professionals can use machine learning algorithms to label images containing known diseases or disorders. However, because images vary from person to person, it is impossible to categorize all potential causes for concern. Once trained, these algorithms can process patient information, perform inferences on unlabeled images, and flag potential reasons for concern.

Fraud Detection

Predictive algorithms can use semi-supervised learning, which requires both labeled and unlabeled data to detect fraud. A user’s credit card activity is labeled and can be used to detect unusual spending patterns.

However, fraud detection solutions do not rely solely on transactions previously classified as fraudulent. We may also make assumptions based on user behavior, including current location, login device, and other factors that require unlabeled data.

Observability of anomaly detection

Anomaly detection is driven by solutions and tools that provide greater observability of performance data. These tools help you quickly identify anomalies to prevent and resolve problems. IBM® Instana™ Observability leverages artificial intelligence and machine learning to provide every team member with a detailed, contextual picture of performance data, helping them accurately predict errors and proactively resolve issues.

IBM watsonx.ai™ provides powerful generative AI tools to analyze large data sets to extract meaningful insights. Through fast, comprehensive analysis, IBM watson.ai can identify patterns and trends that can be used to detect current anomalies and predict future outliers. Watson.ai can be used across industries for a variety of business needs.

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