View source for 18109710. MACHINE LEARNING SYSTEMS AND METHODS FOR CLASSIFICATION simplified abstract (Samsung Display Co., LTD.)

=MACHINE LEARNING SYSTEMS AND METHODS FOR CLASSIFICATION=


==Organization Name==

[[:Category:Samsung Display Co., LTD.|Samsung Display Co., LTD.]]


[[Category:Samsung Display Co., LTD.]]

==Inventor(s)==

[[:Category:Qisen Cheng of San Jose CA (US)|Qisen Cheng of San Jose CA (US)]][[Category:Qisen Cheng of San Jose CA (US)]]

[[:Category:Shuhui Qu of San Jose CA (US)|Shuhui Qu of San Jose CA (US)]][[Category:Shuhui Qu of San Jose CA (US)]]

[[:Category:Kaushik Balakrishnan of San Jose CA (US)|Kaushik Balakrishnan of San Jose CA (US)]][[Category:Kaushik Balakrishnan of San Jose CA (US)]]

[[:Category:Janghwan Lee of San Jose CA (US)|Janghwan Lee of San Jose CA (US)]][[Category:Janghwan Lee of San Jose CA (US)]]

==MACHINE LEARNING SYSTEMS AND METHODS FOR CLASSIFICATION - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18109710 titled 'MACHINE LEARNING SYSTEMS AND METHODS FOR CLASSIFICATION

==Simplified Explanation==

The patent application describes a classification system that calculates reference Shapley values for features of a data sample based on a first classification model, and then trains a second classification model using multi-task distillation to predict Shapley values for the features based on the reference values and a distillation loss, as well as predict a class label for the data sample based on the predicted Shapley values and a ground truth class label.

* The system includes one or more processors and memory with instructions for executing the described processes.
* The first classification model is used to calculate reference Shapley values for the features of a data sample.
* The second classification model is trained through multi-task distillation to predict Shapley values and class labels for the data sample.

== Potential Applications ==
This technology could be applied in various fields such as finance, healthcare, and marketing for improving classification models and feature importance analysis.

== Problems Solved ==
This technology helps in improving the interpretability and accuracy of classification models by incorporating Shapley values for feature importance.

== Benefits ==
The system provides a more accurate understanding of feature importance in classification models, leading to better decision-making and model performance.

== Potential Commercial Applications ==
Potential commercial applications include predictive analytics software, fraud detection systems, and personalized recommendation engines.

== Possible Prior Art ==
Prior art may include research on Shapley values in machine learning models and multi-task learning techniques for model distillation.

== Unanswered Questions ==
=== How does this technology compare to existing methods for feature importance analysis in classification models? ===
The article does not provide a direct comparison to existing methods for feature importance analysis, leaving the reader to wonder about the specific advantages of this approach.

=== What are the potential limitations or challenges in implementing this classification system in real-world applications? ===
The article does not address potential limitations or challenges that may arise when implementing this technology in practical settings, leaving room for further exploration of its feasibility and scalability.


==Original Abstract Submitted==

A classification system includes: one or more processors; and memory including instructions that, when executed by the one or more processors, cause the one or more processors to: calculate reference Shapley values for features of a data sample based on a first classification model; and train a second classification model though multi-task distillation to: predict Shapley values for the features of the data sample based on the reference Shapley values and a distillation loss; and predict a class label for the data sample based on the predicted Shapley values and a ground truth class label for the data sample.



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