A Statistical Approach to Increase Classification Accuracy in Supervised Learning Algorithms
This addresses the problem of limited training data for researchers and practitioners in machine learning, though it appears incremental as it builds on existing mixture model techniques.
The paper tackles the challenge of improving classification accuracy in supervised learning by using probabilistic mixture models to identify sub-labels and generate synthetic training data, resulting in enhanced classification performance.
Probabilistic mixture models have been widely used for different machine learning and pattern recognition tasks such as clustering, dimensionality reduction, and classification. In this paper, we focus on trying to solve the most common challenges related to supervised learning algorithms by using mixture probability distribution functions. With this modeling strategy, we identify sub-labels and generate synthetic data in order to reach better classification accuracy. It means we focus on increasing the training data synthetically to increase the classification accuracy.