18060749. SYSTEMS AND METHODS FOR BAGGING ENSEMBLE CLASSIFIERS FOR IMBALANCED BIG DATA simplified abstract (Capital One Services, LLC)
Contents
SYSTEMS AND METHODS FOR BAGGING ENSEMBLE CLASSIFIERS FOR IMBALANCED BIG DATA
Organization Name
Inventor(s)
Michael Langford of Plano TX (US)
SYSTEMS AND METHODS FOR BAGGING ENSEMBLE CLASSIFIERS FOR IMBALANCED BIG DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 18060749 titled 'SYSTEMS AND METHODS FOR BAGGING ENSEMBLE CLASSIFIERS FOR IMBALANCED BIG DATA
The patent application describes a method for bagging ensemble classifiers for imbalanced big data. The system generates machine learning base models based on user input and trains them iteratively by determining chunks of data containing minority and majority cases.
- Method for bagging ensemble classifiers for imbalanced big data
- System generates machine learning base models based on user input
- Trains machine learning base models iteratively with chunks of data containing minority and majority cases
Potential Applications: - Data analysis in industries with imbalanced datasets - Fraud detection in financial institutions - Medical diagnosis in healthcare
Problems Solved: - Addressing imbalanced data issues in machine learning - Improving accuracy of classifiers in skewed datasets
Benefits: - Enhanced performance in handling imbalanced data - Increased accuracy in predicting minority class outcomes
Commercial Applications: Title: "Enhancing Machine Learning Accuracy for Imbalanced Data Sets" This technology can be used in various industries such as finance, healthcare, and e-commerce to improve predictive modeling and decision-making processes.
Prior Art: No prior art information provided.
Frequently Updated Research: No information on frequently updated research related to this technology.
Questions about Bagging Ensemble Classifiers for Imbalanced Big Data:
Question 1: How does this method improve the accuracy of machine learning models on imbalanced datasets? Answer: This method improves accuracy by training base models iteratively with chunks of data containing minority and majority cases, allowing for better handling of imbalanced data.
Question 2: What are the potential challenges in implementing this method in real-world applications? Answer: Some potential challenges could include the need for large computational resources and the complexity of integrating this method into existing machine learning systems.
Original Abstract Submitted
Disclosed embodiments may include a method for bagging ensemble classifiers for imbalanced big data. The system may receive user input comprising a number of machine learning base models to generate. The system may generate the machine learning base models based on the user input. Iteratively for each machine learning base model of the machine learning base models until all machine learning base models are trained, the system may: determine a chunk for a machine learning base model of the machine learning base models, wherein the chunk comprises all minority cases from training data and a plurality of majority cases from the training data and train the machine learning base model with the chunk.
