What is Isolation Forest (AI)?
Isolation Forest (iForest) is an unsupervised anomaly detection algorithm that works by isolating anomalies from normal instances in a dataset based on their unique statistical properties. It builds a collection of randomized decision trees, where each tree recursively partitions the input space along randomly selected feature dimensions and split points until reaching a leaf node. Anomalous instances are expected to be isolated more quickly than normal instances due to their distinct characteristics or rarity in the dataset.
Isolation Forest computes an anomaly score for each sample in the dataset by averaging the path lengths required to isolate that sample across all decision trees in the ensemble. The shorter the average path length, the higher the likelihood that the sample is an anomaly. Researchers can then set a threshold value on this anomaly score to classify samples as either normal or anomalous based on their degree of isolation within the dataset.
Some key advantages of iForest include:
- Scalability — iForest has linear time complexity in the size of the input dataset, making it suitable for handling large-scale or high-dimensional data.
- Robustness to noise and irrelevant features — iForest is relatively insensitive to outliers or irrelevant variables within the dataset, as its performance depends primarily on the relative isolation of anomalous instances rather than their absolute distances from normal instances.
- Adaptability to various types of data distributions — iForest can be applied to different kinds of input data (e.g., continuous, discrete) and is capable of detecting various forms of anomalies (e.g., point, contextual).
- Interpretability — iForest provides a set of decision paths for each sample in the dataset, which can be used to visualize and analyze the underlying structure or patterns within the input data.
Isolation Forest has been successfully applied to various anomaly detection tasks in diverse domains such as credit fraud detection, network intrusion detection, medical diagnosis, and industrial fault detection. However, it may not perform well on datasets with a very low anomaly rate (e.g., less than 1%), as the algorithm could struggle to differentiate between normal and anomalous instances due to their similar statistical properties. Additionally, iForest requires tuning several hyperparameters such as the number of decision trees and the maximum depth or size of each tree, which can affect its overall performance and efficiency on specific datasets.