Full High-Dimensional Intelligible Learning In 2-D Lossless
  Visualization Space
Boris Kovalerchuk Hoang Phan
This study explores a new methodology for machine learning classification
tasks in 2-dimensional visualization space (2-D ML) using Visual knowledge
Discovery in lossless General Line Coordinates. It is shown that this is a full
machine learning approach that does not require processing n-dimensional data
in an abstract n-dimensional space. It enables discovering n-D patterns in 2-D
space without loss of n-D information using graph representations of n-D data
in 2-D. Specifically, this study shows that it can be done with static and
dynamic In-line Based Coordinates in different modifications, which are a
category of General Line Coordinates. Based on these inline coordinates,
classification and regression methods were developed. The viability of the
strategy was shown by two case studies based on benchmark datasets (Wisconsin
Breast Cancer and Page Block Classification datasets). The characteristics of
page block classification data led to the development of an algorithm for
imbalanced high-resolution data with multiple classes, which exploits the
decision trees as a model design facilitator producing a model, which is more
general than a decision tree. This work accelerates the ongoing consolidation
of an emerging field of full 2-D machine learning and its methodology. Within
this methodology the end users can discover models and justify them as
self-service. Providing interpretable ML models is another benefit of this
approach.