17945391. SYSTEMS AND METHODS FOR SUCCESSIVE FEATURE IMPUTATION USING MACHINE LEARNING simplified abstract (Capital One Services, LLC)
Contents
- 1 SYSTEMS AND METHODS FOR SUCCESSIVE FEATURE IMPUTATION USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS AND METHODS FOR SUCCESSIVE FEATURE IMPUTATION USING MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
SYSTEMS AND METHODS FOR SUCCESSIVE FEATURE IMPUTATION USING MACHINE LEARNING
Organization Name
Inventor(s)
Michael Langford of Plano TX (US)
SYSTEMS AND METHODS FOR SUCCESSIVE FEATURE IMPUTATION USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 17945391 titled 'SYSTEMS AND METHODS FOR SUCCESSIVE FEATURE IMPUTATION USING MACHINE LEARNING
Simplified Explanation
The patent application describes systems and methods for imputing missing feature values in datasets using machine learning techniques.
- The innovation involves sequentially filling in missing feature values in partially-filled datasets by leveraging machine learning algorithms.
- The method utilizes information from populated records within the dataset to predict and impute missing values accurately.
Potential Applications
The technology can be applied in various machine learning contexts and applications where datasets have missing values.
Problems Solved
1. Addressing missing values in datasets to ensure accurate analysis and modeling. 2. Improving the quality and completeness of datasets for machine learning tasks.
Benefits
1. Enhanced accuracy in data analysis and modeling. 2. Efficient handling of missing values in datasets. 3. Improved performance of machine learning algorithms.
Potential Commercial Applications
Optimizing data preprocessing in industries such as finance, healthcare, and marketing for better decision-making and predictive modeling.
Possible Prior Art
There are existing methods for imputing missing values in datasets, such as mean imputation, mode imputation, and regression imputation. However, the disclosed innovation focuses on leveraging machine learning to sequentially fill in missing values in partially-filled datasets.
Unanswered Questions
How does this method compare to traditional imputation techniques?
The article does not provide a direct comparison between the proposed method and traditional imputation techniques. It would be beneficial to understand the performance differences and advantages of using machine learning for imputing missing values.
What types of machine learning algorithms are used in this method?
The article mentions using machine learning algorithms but does not specify which ones are employed. Knowing the specific algorithms utilized could provide insights into the effectiveness and applicability of the method.
Original Abstract Submitted
Systems and methods for successively imputing missing feature values using machine learning to sequentially fill in missing feature values in partially-filled datasets, and by using the information in populated records of the dataset. The systems and methods disclosed herein may be useful in many machine learning contexts and application where datasets are missing values.
