Abstract
Learning from massive amounts of domain-specific information requires new algorithms and models for parsing the ever-expanding field of big data. Such algorithms for exploring and identifying key features in vast databases require analysis of complex interactions to uncover critical features under a variety of circumstances. We study a comprehensive collection of health-related data, showing that our novel Choquet Integral activation function for deep neural networks transforms high-dimensional data into simpler sub-feature sets that better model complex interactions. While standard methods account for unitary feature tracking, they do not extend to multiple feature subsets, an impactful and necessary knowledge base. To this end, our novel activation function creates a sub-additive tool that better considers the weighted compilation of features within a robust set of standard benchmarks, advancing the synergistic and antagonistic relationships among features, capturing non-linear dependencies. We present the theoretical underpinnings, highlighting balanced fuzzy measures and sub-additivity for an optimized model based on real-world health data targeting weight loss. We further test different model settings, akin to hyper-parameter optimization. Despite computational time consumption, which could be improved via nowadays more powerful computing units, this novel method can be implemented as a pre-trained model using big data to identify heretofore unknown sub-additive feature interactions in a variety of fields such as biomedicine, fraud detection, cyber-security, and finance.