Isolation Forest, also known as iForest, is a machine learning algorithm designed to detect outliers in a dataset. It works by randomly selecting and isolating points in the data, which are then used to generate a decision tree. This tree is then used to find anomalous points in the data, which are considered to be outliers. By randomly sampling and selecting points, Isolation Forest attempts to find the outliers in the data without considering the overall distribution of the data.
Isolation Forest was first proposed by Fei Tony Liu, Kai Ming Ting and Zhi-Hua Zhou in their paper “Isolation Forest”. The algorithm is based on the idea of isolating a given data point in the data set by randomly selecting subsequent points in the data. This randomized selection isolates the original data point, which is then used to create a decision tree. This tree is then used to identify outlier points in the data set. The paper states that the algorithm outperforms existing methods for anomoly detection, such as densiy based methods or nearest neighbour methods, in terms of efficiency, accuracy and scalability.
The algorithm works by building a forest of isolation trees. These trees are built by randomly selecting and isolating data points in the dataset. This process is repeated until each data point is isolated. The resultant tree is then used to identify outlier points. This is done by comparing the depths of previously isolated points in the trees. Deeper points are more likely to be outliers, and thus are identified as such.
The utilization of Isolation Forests has grown exponentially since the algorithm was initially proposed. It is now being used in a variety of application, including fraud detection, intrusion detection, outlier detection, and medical diagnosis.
Overall, Isolation Forest is a machine learning algorithm that is used to detect outliers in a dataset. It has become a popular algorithm due to its scalability, accuracy and efficiency, and is being used in a variety of disciplines.
