Hands-On Machine Learning with C++

Detecting anomalies with the Local Outlier Factor method

The distance measurement-based methods are widely used for solving different machine learning problems, as well as for anomaly detection. These methods assume that there is a specific metric in the object space that helps us find anomalies. The general assumption when we use distance-based methods for anomaly detection is that the anomaly only has a few neighbors, while a normal point has many. Therefore, for example, the distance to the k^th neighbor can serve as a good measure of anomalies, as reflected in the Local Outlier Factor (LOF) method. This method is based on estimating the density of objects that have been checked for anomalies. Objects lying in the areas of lowest density ...