Sparse Supervised Representation-Based Classifier for Uncontrolled and Imbalanced Classification.

Area of Science:

• Machine Learning
• Computer Vision
• Pattern Recognition

Background:

• Sparse Representation-based Classification (SRC) is effective but sensitive to data distribution.
• Existing SRC methods struggle with uncontrolled and imbalanced datasets.
• There is a need for robust classification algorithms that handle data variability.

Purpose of the Study:

• To propose a novel Sparse Supervised Representation Classifier (SSRC) to overcome limitations of traditional SRC.
• To enhance SRC's performance on uncontrolled datasets by incorporating class label information.
• To address imbalanced classification challenges within the SSRC framework.

Main Methods:

• SSRC integrates class label information during test sample representation.
• Each class attempts to linearly represent the test sample within its subspace.
• A class weight learning model is incorporated to handle imbalanced data, with weights derived from training samples.

Main Results:

• Experimental validation on the AR face database (uncontrolled) and 15 KEEL datasets (imbalanced).
• Demonstrated significant improvement in classification accuracy for uncontrolled datasets.
• Effectively classified imbalanced datasets with imbalance rates up to 61.18.

Conclusions:

• SSRC offers a robust solution for classification tasks involving uncontrolled and imbalanced data.
• The proposed method enhances the applicability of sparse representation techniques.
• SSRC provides a more reliable machine learning model for real-world, variable data distributions.