学术文献库

Based on structured data derived from large complex systems, we computationally further develop and refine a major factor selection protocol by accommodating structural dependency and heterogeneity among many features to unravel data's information content. Two operational concepts: ``de-associating'' and its counterpart ``shadowing'' that play key roles in our protocol, are reasoned, explained, and carried out via contingency table platforms. This protocol via ``de-associating'' capability would manifest data's information content by identifying which covariate feature-sets do or don't provide information beyond the first identified major factors to join the collection of major factors as secondary members. Our computational developments begin with globally characterizing a complex system by structural dependency between multiple response (Re) features and many covariate (Co) features. We first apply our major factor selection protocol on a Behavioral Risk Factor Surveillance System (BRFSS) data set to demonstrate discoveries of localities where heart-diseased patients become either majorities or further reduced minorities that sharply contrast data's imbalance nature. We then study a Major League Baseball (MLB) data set consisting of 12 pitchers across 3 seasons, reveal detailed multiscale information content regarding pitching dynamics, and provide nearly perfect resolutions to the Multiclass Classification (MCC) problem and the difficult task of detecting idiosyncratic changes of any individual pitcher across multiple seasons. We conclude by postulating an intuitive conjecture that large complex systems related to inferential topics can only be efficiently resolved through discoveries of data's multiscale information content reflecting the system's authentic structural dependency and heterogeneity.