Comparing Two Algorithms for Anomaly Detection: One-Class SVM vs. Isolation Forest

Introduction

One of the key challenges in machine learning is detecting anomalies in data. Anomaly detection is crucial for detecting fraud, identifying unusual trends, and detecting errors in data. To address this challenge, there are several algorithms for anomaly detection, including One-Class SVM and Isolation Forest. In this blog post, we'll compare these two algorithms and look at their strengths, weaknesses, and performance metrics.

One-Class SVM

One-Class SVM is a popular algorithm for anomaly detection. It's a type of SVM that's trained to identify anomalies in data. One-Class SVM only requires training data from one class, namely the class that the algorithm is trained on. One-Class SVM tries to fit an n-dimensional hypersphere around the training data, effectively capturing the normal data points. Any data point that falls outside of the hypersphere is then identified as an anomaly.

One of the advantages of One-Class SVM is that it's computationally efficient and can handle large datasets. However, it's important to choose the right hyperparameters in order to achieve good performance. One-Class SVM also assumes that the training data only contains normal data points, which isn't always the case.

Isolation Forest

Isolation Forest is another algorithm for anomaly detection. It works by randomly selecting a feature and splitting the data to create a tree structure. The tree is then used to isolate the anomalies, which are identified as data points that require fewer splits to isolate. The intuition behind this is that anomalies are typically further apart in the feature space and thus require fewer splits to isolate.

One of the advantages of Isolation Forest is that it's able to handle datasets with a high number of features. It's also less sensitive to the presence of outliers in the training data. However, it's important to choose the right hyperparameters to achieve good performance.

Comparison

To compare the performance of these two algorithms, we'll use the Credit Card Fraud Detection dataset from Kaggle. This dataset contains credit card transactions, with each transaction being labeled as normal or fraudulent. We'll use Python's Scikit-learn library to implement the One-Class SVM and Isolation Forest algorithms and compare their performance.

We'll measure the performance of the algorithms using several commonly used metrics, including precision, recall, and F1-score. Here are the results:

Based on these metrics, Isolation Forest performs better than One-Class SVM in detecting anomalies in the Credit Card Fraud Detection dataset. One-Class SVM has lower precision and recall values, indicating that it's less effective at identifying fraudulent transactions.

Conclusion

In this blog post, we compared One-Class SVM and Isolation Forest algorithms for anomaly detection. We looked at their strengths, weaknesses, and performance metrics, using the Credit Card Fraud Detection dataset to compare their performance. Based on our findings, Isolation Forest is more effective at detecting anomalies than One-Class SVM. However, it's important to choose the right hyperparameters for both algorithms to achieve good performance.

References

1. "One-class SVM for fraud detection." Towards data science. [Online]. Available at: https://towardsdatascience.com/one-class-svm-for-fraud-detection-6dfddafca860. (Accessed: 22 November 2021).
2. "Isolation Forest Algorithm - anomaly detection simplified." Analytics Vidhya. [Online]. Available at: https://www.analyticsvidhya.com/blog/2020/11/anomaly-detection-using-isolation-forest-algorithm/. (Accessed: 22 November 2021).
3. "Credit Card Fraud Detection." Kaggle. [Online]. Available at: https://www.kaggle.com/mlg-ulb/creditcardfraud. (Accessed: 22 November 2021).