Capital one services, llc (20240346506). SYSTEMS AND METHODS FOR OUTLIER DETECTION USING UNSUPERVISED MACHINE LEARNING MODELS TRAINED ON OVERSAMPLED DATA simplified abstract
SYSTEMS AND METHODS FOR OUTLIER DETECTION USING UNSUPERVISED MACHINE LEARNING MODELS TRAINED ON OVERSAMPLED DATA
Organization Name
Inventor(s)
Hassan Shallal of Plano TX (US)
Rajesh Kanna Durairaj of Plano TX (US)
SYSTEMS AND METHODS FOR OUTLIER DETECTION USING UNSUPERVISED MACHINE LEARNING MODELS TRAINED ON OVERSAMPLED DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240346506 titled 'SYSTEMS AND METHODS FOR OUTLIER DETECTION USING UNSUPERVISED MACHINE LEARNING MODELS TRAINED ON OVERSAMPLED DATA
- Simplified Explanation:**
The patent application describes methods and systems for outlier detection using machine learning models, including a two-tier model with majority and minority expert models. The system can generate the minority expert model by training an unsupervised machine learning model on oversampled training data, including synthetic sample outlier events.
- Key Features and Innovation:**
- Outlier detection system with single-tier or two-tier machine learning models - Minority expert model generated from unsupervised machine learning on oversampled data - Binary or multi-class results for outlier detection - Applicable to sequence-based or non-sequence-based events
- Potential Applications:**
- Fraud detection in financial transactions - Anomaly detection in network traffic - Quality control in manufacturing processes
- Problems Solved:**
- Identifying outliers in large datasets - Improving accuracy of outlier detection - Handling imbalanced data in outlier detection
- Benefits:**
- Enhanced accuracy in outlier detection - Efficient handling of imbalanced data - Scalable for large datasets
- Commercial Applications:**
Potential commercial applications include fraud detection systems for financial institutions, anomaly detection systems for cybersecurity companies, and quality control systems for manufacturing industries.
- Prior Art:**
Prior art related to outlier detection using machine learning models can be found in research papers and patents related to anomaly detection and fraud detection systems.
- Frequently Updated Research:**
Stay updated on research in machine learning for outlier detection, advancements in unsupervised learning algorithms, and applications of outlier detection in various industries.
- Questions about Outlier Detection:**
1. How does the system handle imbalanced data in outlier detection? 2. What are the potential challenges in implementing a two-tier machine learning model for outlier detection?
Original Abstract Submitted
methods and systems are described herein for outlier detection. the system may apply a single-tier including a minority expert model only or a two-tier machine learning model, including a majority expert model and a minority expert model. the system may generate the minority expert model by training an unsupervised machine learning model on oversampled training data, including synthetic sample outlier events. in some embodiments, the minority expert model may provide a binary result indicating an event belongs to an outlier category or not. in some embodiments, the minority expert model may include multiple component models providing a multi-class result indicating whether an event belongs to a sub-category of the outlier category. in application, the system may perform the outlier detection on events that are sequence-based or not.
