FORCE: Feature-Oriented Representation with Clustering and Explanation
This work addresses the challenge of enhancing discriminative capability in deep learning models for real-life datasets, though it appears incremental as it builds on existing SHAP and attention mechanisms.
The paper tackles the problem of improving predictive model accuracy by learning latent patterns in data, proposing FORCE, a SHAP-based supervised deep learning framework that uses SHAP values for clustering and attention, resulting in dramatic performance improvements, such as increasing the F1 score for heart disease detection from 0.72 to 0.80.
Learning about underlying patterns in data using latent unobserved structures to improve the accuracy of predictive models has become an active avenue of deep learning research. Most approaches cluster the original features to capture certain latent structures. However, the information gained in the process can often be implicitly derived by sufficiently complex models. Thus, such approaches often provide minimal benefits. We propose a SHAP (Shapley Additive exPlanations) based supervised deep learning framework FORCE which relies on two-stage usage of SHAP values in the neural network architecture, (i) an additional latent feature to guide model training, based on clustering SHAP values, and (ii) initiating an attention mechanism within the architecture using latent information. This approach gives a neural network an indication about the effect of unobserved values that modify feature importance for an observation. The proposed framework is evaluated on three real life datasets. Our results demonstrate that FORCE led to dramatic improvements in overall performance as compared to networks that did not incorporate the latent feature and attention framework (e.g., F1 score for presence of heart disease 0.80 vs 0.72). Using cluster assignments and attention based on SHAP values guides deep learning, enhancing latent pattern learning and overall discriminative capability.