Algorithm Resists Outliers to Provide Accurate Data Classification for Random Sets of Data
The Correntropy Loss, or C-loss, function stabilizes signals in artificial neural networks to optimally classify random data points and resist the presence of outliers in a given sample. Artificial neural networks are statistical models that process information in a way comparable to biological neural networks. Correntropy (correlative entropy) is a nonlinear measurement of similarity between two random variables in a data set. Current existing functions for data classification, including Mean Squared Error (MSE) Loss, contain unfavorable noise levels due to the inclusion of outliers and, therefore, commonly formulate poor models for predicting future trends, an occurrence known as overfitting. These problematic data approximations decrease the success of artificial neural network classification utilized in medical diagnoses, data mining applications, financial data domains, and the informational technology (IT) industry. These losses may result in detrimental miscalculations including medical misdiagnosis, considerable financial losses for companies and private investors, and stunted technological growth. Researchers at the University of Florida have discovered that the Correntropy Loss function demonstrates indifference towards immaterial noise and eliminates the trend bias created by outliers in a data set, thus creating a smooth and accurate function approximation for robust training in artificial neural networks.
C-Loss function in data mining improves data classification in artificial neural networks
- Decreases overfitting with more efficient classification, providing more accurate training for computer systems in the IT, medical, and financial industries
- Easy-to-use application shifts simply between MSE-Loss and C-Loss training, minimizing computational complexities while transferring over to C-Loss
- Increases data mining and classification precision while maintaining implementation costs, allowing for an improved artificial neural network for same cost
- Improves performance of artificial neural network classifiers while minimizing empirical risk while, permitting for more exact data prediction and analysis
The Correntropy Loss function is an advanced statistical algorithm utilized for precise data mining and classification in artificial neural networks. The MSE-Loss function is currently one of the most common formulas utilized for nonlinear, non-Gaussian distribution data in computational network classification; however, this formula does not take outlier data into account and is limited by complexity. The Correntropy loss function, when combined with the MSE-loss function, constructs an algorithm for precise data classification and mining in artificial neural networks. The C-Loss function is insensitive to outliers and resilient to overfitting, and utilizes the application of Correntropy to a known set of values and training samples to obtain a discriminant function. That function is then employed to accurately predict the performance of test values. Since a plethora of current database systems employ the MSE-loss function for training, a simple switch over to the C-loss function may prove beneficial in terms of improving measurement accuracy, robust training, data mining, and data classification in neural networks.