A Feature Selection Layer Approach for Multi-Class Classification on Neural Networks

Key points

• In the age of data-driven decision-making, deep learning models have become powerful tools for uncovering patterns and making predictions that influence many aspects of society.1
• Feature weighting methods, which assess the relevance of individual features, are among the most widely studied approaches for improving interpretability.1
• Embedded techniques integrate feature relevance learning directly into the model training process, but they are often unable to capture label-specific relationships.1
• Our results show that the proposed method matches existing techniques in identifying globally relevant features and outperforms them in capturing label-specific relevance.1

Sources (1)

• [1]A Feature Selection Layer Approach for Multi-Class Classification on Neural Networks
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 04:01 AM

  To address the limitation, we propose multiple extensions to the Feature Selection Layer, an embedded feature weighting mechanism for neural networks.

  In the age of data-driven decision-making, deep learning models have become powerful tools for uncovering patterns and making predictions that influence many aspects of society.

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