17938162. ENHANCING SILENT FEATURES WITH ADVERSARIAL NETWORKS FOR IMPROVED MODEL VERSIONS simplified abstract (International Business Machines Corporation)

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ENHANCING SILENT FEATURES WITH ADVERSARIAL NETWORKS FOR IMPROVED MODEL VERSIONS

Organization Name

International Business Machines Corporation

Inventor(s)

Kavitha Hassan Yogaraj of Bangalore (IN)

Shantanu Sinha of Kolkata (IN)

Amit Kumar Raha of Kolkata (IN)

Shikhar Kwatra of San Jose CA (US)

Debajyoti Bagchi of Kolkata (IN)

Aaron K. Baughman of Cary NC (US)

ENHANCING SILENT FEATURES WITH ADVERSARIAL NETWORKS FOR IMPROVED MODEL VERSIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17938162 titled 'ENHANCING SILENT FEATURES WITH ADVERSARIAL NETWORKS FOR IMPROVED MODEL VERSIONS

Simplified Explanation

The patent application describes techniques for enhancing silent features with adversarial networks to improve model versions. Here is a simplified explanation of the abstract:

  • Input features are obtained.
  • Hidden features are identified.
  • Quantum feature importance scoring is performed to assign an importance score to each hidden feature.
  • Silent features are identified as hidden features with an importance score below a first threshold.
  • Important features are identified as input features and hidden features with an importance score above a second threshold.
  • A silent feature model is built using the silent features.
  • An important feature model is built using the important features.
  • An ensemble model is built with the silent feature model and the important feature model.
  • The ensemble model is used to generate predictions and prescriptions.
      1. Potential Applications

This technology can be applied in various fields such as healthcare, finance, and marketing for predictive modeling and decision-making processes.

      1. Problems Solved

This technology helps in identifying important features in datasets, improving model performance, and enhancing the interpretability of machine learning models.

      1. Benefits

The benefits of this technology include increased accuracy in predictions, better understanding of model decisions, and improved overall model performance.

      1. Potential Commercial Applications

This technology can be utilized in industries such as healthcare for personalized medicine, finance for risk assessment, and marketing for targeted advertising campaigns.

      1. Possible Prior Art

One possible prior art could be the use of adversarial networks in machine learning models to enhance feature importance and model performance.

        1. Unanswered Questions
        2. How does this technology compare to traditional feature selection methods?

This article does not provide a direct comparison between this technology and traditional feature selection methods. It would be interesting to see a study or analysis that compares the effectiveness and efficiency of this approach against more conventional methods.

        1. What are the potential limitations or drawbacks of using adversarial networks for feature enhancement?

The article does not delve into the potential limitations or drawbacks of using adversarial networks for feature enhancement. It would be beneficial to understand any challenges or constraints that may arise when implementing this technology in practical applications.


Original Abstract Submitted

Provided are techniques for enhancing silent features with adversarial networks for improved model versions. Input features are obtained. Hidden features are identified. Quantum feature importance scoring is performed to assign an importance score to each of the hidden features. Silent features are identified as the hidden features with the importance score below a first threshold. Important features are identified as the input features and as the hidden features with the importance score above a second threshold. A silent feature model is built using the silent features. An important feature model is built using the important features. An ensemble model is built with the silent feature model and the important feature model. The ensemble model is used to generate one or more predictions and one or more prescriptions.

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