Fundamental Laws of Binary Classification

TL;DR

This paper introduces a geometric and statistical framework for binary classification, showing that optimal discriminant functions are solutions to locus equations representing decision boundaries, and that such systems achieve equilibrium by balancing eigenenergy and risk.

Contribution

It presents a novel geometric locus approach to binary classification, linking discriminant functions to eigenaxes and demonstrating how systems minimize risk and eigenenergy through equilibrium.

Findings

Discriminant functions are solutions to geometric locus equations.

Optimal systems balance eigenenergy and risk at equilibrium.

Minimum risk systems exhibit symmetrical eigenaxis properties.

Abstract

Finding discriminant functions of minimum risk binary classification systems is a novel geometric locus problem -- which requires solving a system of fundamental locus equations of binary classification -- subject to deep-seated statistical laws. We show that a discriminant function of a minimum risk binary classification system is the solution of a locus equation that represents the geometric locus of the decision boundary of the system, wherein the discriminant function is connected to the decision boundary by an exclusive principal eigen-coordinate system -- at which point the discriminant function is represented by a geometric locus of a novel principal eigenaxis -- structured as a dual locus of likelihood components and principal eigenaxis components. We demonstrate that a minimum risk binary classification system acts to jointly minimize its eigenenergy and risk by locating a point of equilibrium, at which point critical minimum eigenenergies exhibited by the system are symmetrically concentrated in such a manner that the novel principal eigenaxis of the system exhibits symmetrical dimensions and densities, so that counteracting and opposing forces and influences of the system are symmetrically balanced with each other -- about the geometric center of the locus of the novel principal eigenaxis -- whereon the statistical fulcrum of the system is located. Thereby, a minimum risk binary classification system satisfies a state of statistical equilibrium -- so that the total allowed eigenenergy and the expected risk exhibited by the system are jointly minimized within the decision space of the system -- at which point the system exhibits the minimum probability of classification error.