Balancing Complex Signals for Robust Predictive Modeling.

Area of Science:

• Machine Learning
• Data Science
• Predictive Modeling

Background:

• Traditional predictive modeling often removes outliers, leading to poor performance on new data with outliers.
• Modern machine learning incorporates outliers but can be inefficient and compromise accuracy.

Purpose of the Study:

• To propose a novel complex signal balancing technique for robust predictive modeling.
• To optimize the inclusion of outliers (complex signals) during data preprocessing for enhanced model performance.

Main Methods:

• Developed a complex signal balancing technique for data preprocessing.
• Determined the optimal value for maximum outlier inclusion to maximize model performance.
• Evaluated models based on accuracy, execution time, and complexity.

Main Results:

• Models preprocessed with the proposed technique demonstrated higher predictive accuracy.
• Improved execution time and reduced model complexity were observed.
• The method effectively incorporates a maximum number of complex signals during training.

Conclusions:

• The proposed complex signal balancing technique enhances predictive modeling by optimizing outlier incorporation.
• This approach offers a superior alternative to traditional outlier removal and modern over-training methods.
• Achieved a balance between accuracy, efficiency, and model complexity in predictive modeling.