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The goal of designing an optimal nearest neighbor classifier is to maximize the classification accuracy while minimizing the sizes of both the reference and feature sets.
In this paper, we proposed a multi-objective Pareto based particle swarm optimization (MOPPSO) to minimize the architectural complexity and maximize the classification accuracy of a polynomial neural network (PNN).
The objective is then to maximize the classification accuracy rate.
This formula represents an optimal classification hyperplane, which can minimize the classification error rate and maximize the classification interval in the meantime.
In TNB, unlike SNB and LCMNB, these discriminant functions can be tuned to maximize the classification ability of the TNB based classifiers by tweaking the value of the (lambda ) parameter in the interval [0,1].
These approaches should reduce the dimensionality of the hyper-spectral data by selecting the most relevant bands that maximize the classification rate (or the separation among materials) without reducing the discriminative power of the extracted features [24].
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We then report the horizon that maximizes the classification ability of the chronology.
The bandwidth parameter 0.15, which maximized the classification accuracy, was chosen for the following experiments.
The optimal τ is selected by maximizing the classification accuracy on 3 unseen points.
Choose the number of neighbors K = 5 that maximizes the classification accuracy.
For a set of training samples, D-ABC algorithm modifies parameters vector x → = { C, γ, q 1, q 2, …, q n } cycle-by-cycle, and search best x → for maximizing the classification accuracy.
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