Artificial Intelligence
Comprehensive analysis of artificial intelligence isolation_forest algorithm technology
With the rising popularity of machine learning in recent years, especially with the rapid development of deep learning, machine learning algorithms have found their way into numerous fields. Recently, while working on an advertising company, I was tasked with developing an anti-cheat algorithm. This led me to consider anomaly detection techniques, and after some research, I came across a widely used method known as Isolation Forest, or iForest.
Proposed by the team at National Taiwan University led by Professor Zhou Zhihua in 2010, Isolation Forest is a powerful and efficient algorithm for anomaly detection. It is particularly effective in handling high-dimensional and large-scale data, making it a popular choice in various industrial applications. Below is a detailed explanation of how this algorithm works.
**iTree Construction**
At the core of Isolation Forest lies the Isolation Tree (iTree), which is essentially a binary tree built using random partitioning. Each node in the tree splits the dataset based on randomly selected features and values. The process continues recursively until one of the stopping conditions is met—either when only one sample remains, or when the tree reaches a predefined maximum height.
The key idea behind iTree is that anomalies tend to be isolated more quickly than normal points. Therefore, the path length from the root to the leaf node where a sample ends up can be used as an indicator of its abnormality. To normalize this path length, a formula is applied:
$$
s(x, n) = 2^{-\frac{h(x)}{c(n)}}
$$
Where $ h(x) $ is the path length of the sample $ x $, and $ c(n) $ is the average path length for a dataset of size $ n $. The value of $ s(x, n) $ ranges between 0 and 1, with higher values indicating a greater likelihood of being an anomaly.
**iForest Construction**
While a single iTree may not be reliable due to its randomness, combining multiple trees into an ensemble significantly improves performance. This is how Isolation Forest is constructed. Instead of using the entire dataset for each tree, a subset is randomly sampled. The paper suggests that sampling sizes larger than 256 offer diminishing returns in terms of performance, while increasing computational costs. By limiting the number of samples and setting a maximum depth for each tree, the algorithm becomes both efficient and effective.
When predicting anomalies, the algorithm computes the average path length across all trees in the forest. This approach allows for a more accurate estimation of anomaly scores.
**Handling High-Dimensional Data**
For high-dimensional datasets, the algorithm can be enhanced by selecting relevant features using statistical measures such as kurtosis. This helps reduce noise and improve the accuracy of the model. Additionally, the algorithm is unsupervised, meaning it does not require labeled data. In cases where anomalies are rare, the model can still be trained using only normal samples, though this may slightly reduce its effectiveness.
**Summary**
Isolation Forest has linear time complexity, making it suitable for large-scale datasets. As an ensemble method, it benefits from adding more trees, which increases stability. Since each tree is built independently, it can be easily parallelized and deployed on distributed systems.
However, the algorithm is not without limitations. It struggles with very high-dimensional data, as it only uses a small portion of the dimensions during tree construction. Additionally, it is better suited for detecting global anomalies rather than local ones. Several improvements have been proposed to address these issues, such as the "Improving iForest with Relative Mass" technique.
Overall, Isolation Forest has made significant contributions to the field of anomaly detection and has been widely recognized in top-tier data mining conferences and journals.
**Note**
Currently, there is no official Java implementation of Isolation Forest available. However, the algorithm is implemented in scikit-learn version 0.18 for Python. Since most of my projects are developed in Java, I had to implement the algorithm myself. The source code is now open-sourced on GitHub. You can download the repository, import it into your IDE, and run the test program to see the algorithm in action.
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