Smart sampling and incremental function learning for very large high dimensional data.

Area of Science:

• Computational Intelligence
• Machine Learning
• Data Science

Background:

• Modern datasets are increasingly large and high-dimensional, posing challenges for data-driven algorithms.
• Existing research primarily addresses classification tasks, with less focus on regression for high-dimensional data.

Purpose of the Study:

• To develop a systematic approach for optimal regression on very large, high-dimensional datasets.
• To introduce smart sampling and incremental function learning for efficient data handling.

Main Methods:

• Utilizing iterative smart sampling to minimize data points while optimally covering input-output spaces.
• Implementing incremental function learning, refining regression models with newly generated samples.
• Assessing approximation accuracy and confidence using the Probably Approximately Correct (PAC) computation framework.

Main Results:

• Demonstrated feasibility and effectiveness on benchmark and real-world functions.
• Showcased the scalability of the proposed techniques for extremely large datasets.
• Achieved accurate function approximation with quantifiable confidence levels.

Conclusions:

• The proposed smart sampling and incremental learning offer a scalable and practical solution for regression on massive, high-dimensional data.
• These computational intelligence techniques enhance the efficiency and accuracy of function approximation in data-intensive applications.