  Machine learning algorithms often produce models considered as complex
black-box models by both end users and developers. They fail to explain the
model in terms of the domain they are designed for. The proposed Iterative
Visual Logical Classifier (IVLC) is an interpretable machine learning algorithm
that allows end users to design a model and classify data with more confidence
and without having to compromise on the accuracy. Such technique is especially
helpful when dealing with sensitive and crucial data like cancer data in the
medical domain with high cost of errors. With the help of the proposed
interactive and lossless multidimensional visualization, end users can identify
the pattern in the data based on which they can make explainable decisions.
Such options would not be possible in black box machine learning methodologies.
The interpretable IVLC algorithm is supported by the Interactive Shifted Paired
Coordinates Software System (SPCVis). It is a lossless multidimensional data
visualization system with user interactive features. The interactive approach
provides flexibility to the end user to perform data classification as
self-service without having to rely on a machine learning expert. Interactive
pattern discovery becomes challenging while dealing with large data sets with
hundreds of dimensions/features. To overcome this problem, this chapter
proposes an automated classification approach combined with new Coordinate
Order Optimizer (COO) algorithm and a Genetic algorithm. The COO algorithm
automatically generates the coordinate pair sequences that best represent the
data separation and the genetic algorithm helps optimizing the proposed IVLC
algorithm by automatically generating the areas for data classification. The
feasibility of the approach is shown by experiments on benchmark datasets
covering both interactive and automated processes used for data classification.
