Isolation Forest Algorithm
Overview
Isolation Forest is an unsupervised machine learning algorithm designed for anomaly detection. Unlike other anomaly detection methods that try to describe "normal" data points, Isolation Forest works by explicitly isolating anomalies instead of profiling normal data points.
How It Works
The algorithm is based on the following insight:
- Random Partitioning: Builds random decision trees by randomly selecting a feature and a split value
- Path Length: Measures how many splits (path length) it takes to isolate a data point
- Anomaly Detection:
- Anomalies are isolated with fewer splits (shorter path lengths)
- Normal points require more splits to be isolated (longer path lengths)
Key Advantages
- Efficient: Linear time complexity O(n), making it suitable for large datasets
- No Need for Labeled Data: Unsupervised approach - doesn't require known anomalies
- Robust to "Swamping": Can effectively distinguish anomalies from normal points
- Handles High-Dimensional Data: Works well with datasets containing many features
When to Use Isolation Forest
Isolation Forest is ideal for:
- Detecting outliers in metabolomics or proteomics data
- Identifying abnormal CBC results that may indicate measurement errors
- Finding anomalous samples in high-dimensional omics datasets
- Data quality assessment before ML pipeline training
- Exploratory data analysis to understand data distributions
Parameters in CMMI-DCC
When using Isolation Forest in ML Pipelines:
- Contamination: Expected proportion of outliers in the dataset (default: 0.1 or 10%)
- Number of Estimators: Number of trees in the forest (default: 100)
- Max Samples: Number of samples to draw to train each tree
Example Use Cases
- Identifying contaminated samples in metabolomics datasets
- Detecting measurement errors in clinical lab results
- Finding outlier participants before cohort analysis
- Quality control in multi-omics data integration
Related Algorithms
- Random Forest: Ensemble method for classification/regression
- Local Outlier Factor (LOF): Density-based outlier detection
- One-Class SVM: Support vector method for anomaly detection