Fundamental Laws of Binary Classification

Finding discriminant functions of minimum risk binary classification systems is a novel geometric locus problem -- that 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 intrinsic eigen-coordinate system in such a manner that the discriminant function is represented by a geometric locus of a novel principal eigenaxis -- formed by 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 wherein critical minimum eigenenergies exhibited by the system are symmetrically concentrated in such a manner that the geometric locus of the novel principal eigenaxis of the system exhibits symmetrical dimensions and densities, such 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 wherein the total allowed eigenenergy and the expected risk exhibited by the system are jointly minimized within the decision space of the system, so that the system exhibits the minimum probability of classification error.