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Based on structured data derived from large complex systems, wecomputationally further develop and refine a major factor selection protocol byaccommodating structural dependency and heterogeneity among many features tounravel data's information content. Two operational concepts:``de-associating'' and its counterpart ``shadowing'' that play key roles in ourprotocol, are reasoned, explained, and carried out via contingency tableplatforms. This protocol via ``de-associating'' capability would manifestdata's information content by identifying which covariate feature-sets do ordon't provide information beyond the first identified major factors to join thecollection of major factors as secondary members. Our computationaldevelopments begin with globally characterizing a complex system by structuraldependency between multiple response (Re) features and many covariate (Co)features. We first apply our major factor selection protocol on a BehavioralRisk Factor Surveillance System (BRFSS) data set to demonstrate discoveries oflocalities where heart-diseased patients become either majorities or furtherreduced minorities that sharply contrast data's imbalance nature. We then studya Major League Baseball (MLB) data set consisting of 12 pitchers across 3seasons, reveal detailed multiscale information content regarding pitchingdynamics, and provide nearly perfect resolutions to the MulticlassClassification (MCC) problem and the difficult task of detecting idiosyncraticchanges of any individual pitcher across multiple seasons. We conclude bypostulating an intuitive conjecture that large complex systems related toinferential topics can only be efficiently resolved through discoveries ofdata's multiscale information content reflecting the system's authenticstructural dependency and heterogeneity.

Multiscale major factor selections for complex system data with structural dependency and heterogeneity