Pairwise costs in multiclass perceptrons

Area of Science:

• Machine Learning
• Pattern Recognition
• Artificial Intelligence

Background:

• Training neural networks often requires careful consideration of misclassification costs.
• Existing methods may not efficiently incorporate pairwise misclassification costs.
• Single-layer perceptrons are fundamental building blocks in neural networks.

Purpose of the Study:

• To introduce a novel loss function for training K single-layer perceptrons (KSLPs).
• To enable direct incorporation of pairwise misclassification cost matrices.
• To improve training efficiency and performance in pattern recognition.

Main Methods:

• Developed a novel loss function for KSLPs.
• Integrated pairwise misclassification cost matrix directly into the loss function.
• Evaluated the method using real-world pattern recognition tasks.

Main Results:

• The novel loss function trains KSLPs without increasing network complexity or gradient computation overhead.
• Training requires fewer epochs for loss minimization.
• The proposed method generally outperforms three benchmark methods in experiments.

Conclusions:

• The novel loss function offers an efficient way to perform cost-sensitive learning in KSLPs.
• Direct incorporation of cost matrices simplifies the training process.
• The approach shows significant promise for practical pattern recognition applications.