View source for 18272949. INDICATOR SELECTION APPARATUS, INDICATOR SELECTION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM simplified abstract (NEC Corporation)

=INDICATOR SELECTION APPARATUS, INDICATOR SELECTION METHOD, AND NON-TRANSITORY COMPUTER-READABLE MEDIUM=


==Organization Name==

[[:Category:NEC Corporation|NEC Corporation]]


[[Category:NEC Corporation]]

==Inventor(s)==

[[:Category:Tomoyuki Nishiyama of Tokyo (JP)|Tomoyuki Nishiyama of Tokyo (JP)]][[Category: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.



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