Arno  Bradtke

Arno Bradtke

1601334000

Anomaly detection with Local Outlier Factor (LOF)

Today’s article is my 5th in a series of “bite-size” article I am writing on different techniques used for anomaly detection. If you are interested, the following are the previous four articles:

Today I am going beyond statistical techniques and stepping into machine learning algorithms for anomaly detection.

#outlier-detection #fraud-detection #data-science #machine-learning #anomaly-detection

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Anomaly detection with Local Outlier Factor (LOF)
Arno  Bradtke

Arno Bradtke

1601334000

Anomaly detection with Local Outlier Factor (LOF)

Today’s article is my 5th in a series of “bite-size” article I am writing on different techniques used for anomaly detection. If you are interested, the following are the previous four articles:

Today I am going beyond statistical techniques and stepping into machine learning algorithms for anomaly detection.

#outlier-detection #fraud-detection #data-science #machine-learning #anomaly-detection

Michael  Hamill

Michael Hamill

1618310820

These Tips Will Help You Step Up Anomaly Detection Using ML

In this article, you will learn a couple of Machine Learning-Based Approaches for Anomaly Detection and then show how to apply one of these approaches to solve a specific use case for anomaly detection (Credit Fraud detection) in part two.

A common need when you analyzing real-world data-sets is determining which data point stand out as being different from all other data points. Such data points are known as anomalies, and the goal of anomaly detection (also known as outlier detection) is to determine all such data points in a data-driven fashion. Anomalies can be caused by errors in the data but sometimes are indicative of a new, previously unknown, underlying process.

#machine-learning #machine-learning-algorithms #anomaly-detection #detecting-data-anomalies #data-anomalies #machine-learning-use-cases #artificial-intelligence #fraud-detection

Art  Lind

Art Lind

1603011600

Outlier Detection with Multivariate Normal Distribution in Python

_All the code files will be available at : _https://github.com/ashwinhprasad/Outliers-Detection/blob/master/Outliers.ipynb

What is an Outlier ?

Anything that is unusual and deviates from the standard “normal” is called an Anomaly or an Outlier.

Detecting these anomalies in the given data is called as anomaly detection.

For more theoretical information about outlier or anomaly detection, Check out :** How Anomaly Detection Works ?**

Why do we need to remove outliers or detect them ?

**Case 1 : **Consider a situation where a big manufacturing company is manufacturing an airplane. An airplane has different parts and we don’t want any parts to behave in an unusual way. these unusual behaviours might be because of various reasons. we want to detect these parts before it is fixed in an airplane else the lives of the passengers might be in danger.

Image for post

**Case 2: **As you can see in the Above Image, how outliers can affect the equation of the line of best fit. So, before performing it is important to remove outliers in order to get the most accurate predictions.

In this post, I will be using Multivariate Normal Distribution

#outlier-detection #anomaly-detection #machine-learning #python #outliers

Wanda  Huel

Wanda Huel

1601528520

What is an Outlier? Algorithms that are affected by outliers.

In statistics, an outlier is an observation point that is distant from other observations.

These extreme values need not necessarily impact the model performance or accuracy, but when they do they are called “Influential” points.

Note: _An _outlier_ is a data point that diverges from an overall pattern in a sample. An influential point is any point that has a large effect on the slope of a regression line._

Now the question arises that how we can detect these outliers and how to handle them?

Well before jumping straight into the solution lets explore that how the outliers being added to our dataset. What is the root cause of it.

#outliers #anomaly-detection #algorithms #outlier-detection #machine-learning

Wanda  Huel

Wanda Huel

1601280960

Statistical techniques for anomaly detection

Anomaly and fraud detection is a multi-billion-dollar industry. According to a Nilson Report, the amount of global credit card fraud alone was USD 7.6 billion in 2010. In the UK fraudulent credit card transaction losses were estimated at more than USD 1 billion in 2018. To counter these kinds of financial losses a huge amount of resources are employed to identify frauds and anomalies in every single industry.

In data science, “Outlier”, “Anomaly” and “Fraud” are often synonymously used, but there are subtle differences. An “outliers’ generally refers to a data point that somehow stands out from the rest of the crowd. However, when this outlier is completely unexpected and unexplained, it becomes an anomaly. That is to say, all anomalies are outliers but not necessarily all outliers are anomalies. In this article, however, I am using these terms interchangeably.

There are numerous reasons why understanding and detecting outliers are important. As a data scientist when we make data preparation we take great care in understanding if there is any data point unexplained, which may have entered erroneously. Sometimes we filter completely legitimate outlier data points and remove them to ensure greater model performance.

There is also a huge industrial application of anomaly detection. Credit card fraud detection is the most cited one but in numerous other cases anomaly detection is an essential part of doing business such as detecting network intrusion, identifying instrument failure, detecting tumor cells etc.

A range of tools and techniques are used to detect outliers and anomalies, from simple statistical techniques to complex machine learning algorithms, depending on the complexity of data and sophistication needed. The purpose of this article is to summarise some simple yet powerful statistical techniques that can be readily used for initial screening of outliers. While complex algorithms can be inevitable to use, sometimes simple techniques are more than enough to serve the purpose.

Below is a primer on five statistical techniques.

#anomaly-detection #machine-learning #outlier-detection #data-science #fraud-detection