Machine learning performance validation and training using a 'perfect' expert system.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Computational Science

Background:

• Developing and evaluating machine learning (ML) systems often requires extensive, domain-specific datasets.
• Data collection can be time-consuming, costly, and may not cover all necessary data characteristics or noise levels.
• Existing methods face challenges in providing flexible and generalized testing environments for ML technologies.

Purpose of the Study:

• To introduce a method for generating application domain-agnostic data for ML system training and evaluation.
• To enable the development and testing of ML technologies without the immediate need for compatible real-world data.
• To facilitate robust ML system analysis under various conditions, including controlled noise and perturbations.

Main Methods:

• A random expert system network is generated based on user-defined parameters to model unspecified phenomena.
• This expert system processes random inputs to produce ideal outputs for ML system training and testing.
• The generated data allows for testing ML systems with specific characteristics, including controlled noise and perturbations.

Main Results:

• The proposed method provides a domain-agnostic approach for testing ML technologies, enhancing result generalization.
• It allows ML systems to be tested with data exhibiting diverse characteristics without needing to source specific datasets.
• The approach facilitates testing under conditions of no noise, known noise levels, and other perturbations for detailed analysis.

Conclusions:

• This method offers a flexible and efficient way to develop and validate machine learning systems.
• It supports the creation of ML technologies by providing a means for proof-of-concept validation and operational testing.
• The approach aids in advancing ML research, including system security and adversarial attack analysis.