Comprehensive analysis of artificial intelligence isolation_forest algorithm technology

With the growing popularity of machine learning in recent years, especially with the rise of deep learning, machine learning algorithms have become increasingly prevalent across various industries. Recently, while working on an advertising company, I came across the need for an anti-cheat algorithm and thought about using anomaly detection techniques. After researching online, I discovered a highly effective algorithm known as Isolation Forest, or iForest. Proposed by the team at National Taiwan University led by Zhou Zhihua in 2010, Isolation Forest is a powerful and efficient method for anomaly detection. It is widely used in industry due to its high performance and fast processing capabilities, especially when dealing with high-dimensional and large-scale data. Below is a detailed explanation of how this algorithm works. **iTree Construction** An iForest is made up of multiple iTrees, which are random binary trees. Each node in an iTree has two children, or it's a leaf node. The construction process starts with a dataset D, where all attributes are continuous variables. The steps to build an iTree are as follows: 1. Randomly select an attribute. 2. Randomly choose a value from that attribute. 3. Split the data: records with values less than the chosen value go to the left child, and those greater than or equal go to the right child. 4. Recursively repeat this process until one of the stopping conditions is met: - The dataset contains only one record or identical records. - The tree reaches a predefined maximum height. Once the iTree is built, anomalies can be detected by analyzing the path length from the root to the leaf node. Anomalies tend to be isolated quickly, resulting in shorter path lengths. A normalization formula is used to calculate the anomaly score: $$ s(x,n) = 2^{-\frac{h(x)}{c(n)}} $$ Where $ h(x) $ is the path length, and $ c(n) $ is the average path length for a dataset of size n. This score ranges between 0 and 1, with higher values indicating a greater likelihood of being an anomaly. **iForest Construction** To build an iForest, multiple iTrees are constructed. Unlike Random Forest, where each tree is built using the full dataset, iForest samples a subset of the data for each tree. The sample size is typically much smaller than the total number of records, often around 256, as larger samples do not significantly improve performance and may increase computation time. Additionally, the maximum height of each iTree is set to $ \lceil \log_2(\psi) \rceil $, where $ \psi $ is the sample size. This helps reduce unnecessary computations and improves efficiency. When predicting, the anomaly scores from each iTree are aggregated. The final score for a data point is calculated based on the average path length across all trees. **Handling High-Dimensional Data** For high-dimensional datasets, the algorithm can be improved by selecting relevant features using measures like kurtosis. Instead of using all attributes, a subset is randomly selected to build the iTree, reducing noise and improving accuracy. **Using Only Normal Samples** Since iForest is an unsupervised algorithm, it doesn't require labeled data. Even if abnormal samples are scarce, the algorithm can still be trained using only normal samples, though the performance might slightly decrease. Adjusting the sample size can help improve results. **Summary** Isolation Forest offers linear time complexity, making it suitable for large-scale datasets. The more trees in the forest, the more stable the results. Since each tree is built independently, it can be easily parallelized for faster processing. However, iForest is not ideal for extremely high-dimensional data, as it randomly selects dimensions and may ignore important information. In such cases, subspace-based anomaly detection methods are recommended. It is also primarily sensitive to global anomalies—points that are sparse across the entire dataset—but less effective for local anomalies, which are sparse relative to their neighbors. Some improved versions, such as "Improving iForest with Relative Mass," have been proposed to address these limitations. Overall, iForest has made significant contributions to anomaly detection and mass estimation theory, and it has been widely recognized in top data mining conferences and journals. **Note** Currently, there is no open-source Java library implementing iForest. However, the algorithm is available in Python’s scikit-learn version 0.18. As most of my projects are in Java, I implemented the algorithm myself and made the source code publicly available on GitHub. You can download the code, open it in IntelliJ IDEA, and run the test program to see the algorithm's performance.

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