18272949. INDICATOR SELECTION APPARATUS, INDICATOR SELECTION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM simplified abstract (NEC Corporation)
Contents
- 1 INDICATOR SELECTION APPARATUS, INDICATOR SELECTION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 INDICATOR SELECTION APPARATUS, INDICATOR SELECTION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM - 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 Unanswered Questions
- 1.10 Original Abstract Submitted
INDICATOR SELECTION APPARATUS, INDICATOR SELECTION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
Organization Name
Inventor(s)
Tomoyuki Nishiyama of Tokyo (JP)
INDICATOR SELECTION APPARATUS, INDICATOR SELECTION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18272949 titled 'INDICATOR SELECTION APPARATUS, INDICATOR SELECTION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM
Simplified Explanation
The abstract describes an indicator selection apparatus that uses machine learning to select support variables for category variables in order to improve the accuracy of a model.
- The acquisition unit acquires category variable specification information.
- The selection unit selects support variables for each category variable using machine learning.
- A first model is generated using a combination of category and support variables.
- First influence degrees are calculated to measure the influence of each combination variable on model accuracy.
- Support variables are selected based on the first influence degrees.
Potential Applications
This technology could be applied in various fields such as finance, marketing, and healthcare for data analysis and predictive modeling.
Problems Solved
This technology helps in improving the accuracy of models by selecting the most relevant support variables for each category variable.
Benefits
The benefits of this technology include increased accuracy in predictive modeling, better decision-making based on data analysis, and improved efficiency in selecting support variables.
Potential Commercial Applications
Optimizing Support Variable Selection for Improved Model Accuracy
Unanswered Questions
How does the selection unit determine the optimal support variables for each category variable?
Can this technology be applied to real-time data analysis and decision-making processes?
Original Abstract Submitted
An indicator selection apparatus () includes an acquisition unit () and a selection unit (). The acquisition unit () acquires information (hereinafter, described as category variable specification information) that specifies a category variable. The selection unit () selects a support variable from among the plurality of indicators described above for each category variable. Specifically, the selection unit () generates a first model by performing machine learning by using a combination (combination variable) of the category variable and the support variable as an explanatory variable and using an evaluation result of an evaluation target as an objective variable. Then, a first influence degree is generated for each of a plurality of the combination variables. The first influence degree indicates magnitude of an influence of the combination variable on accuracy of the first model. Then, the selection unit () selects a support variable by using the first influence degree.
