  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.
