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Choose the number of neighbors K = 5 that maximizes the classification accuracy.
We then report the horizon that maximizes the classification ability of the chronology.
In the (K-NN) classifier, we have used a cross validation which is defined as follows: Divide training examples into two sets, a training set (95%%) and a validation set (5%%); Predict the class labels for the validation set by using the examples in the training set; and Choose the number of neighbors K = 5 that maximizes the classification accuracy.
Multivariable filter methods include mRMR [ 9], correlation-based feature selection [ 10], and Markov blanket filter [ 11]; (ii) wrapper methods, which search for an optimal feature set that maximizes the classification performance, defined in terms of an evaluation function (such as cross-validation accuracy).
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The objective is then to maximize the classification accuracy rate.
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.
This formula represents an optimal classification hyperplane, which can minimize the classification error rate and maximize the classification interval in the meantime.
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.
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].
Generally, a cost-based feature selection method is used to maximize the classification performance and minimize the classification cost associated with the features, which is a multi-objective optimization problem.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com