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Secondly, the Kernel Fuzzy C-Means algorithm was used to achieve greater separability among the classes, and reduce the classification errors.
The basic principle of boosting refers to the idea that a model constructed by week learners (e.g., model Fm built by m trees which are numbered from 1 to m) can be modified to become better by adding new tree, as the goal of each iteration step (i.e., generating a new tree) is to reduce the classification error made by current model.
This inconsistency will then reduce the classification performance.
Adaptive boosting technique is employed to reduce the classification error rate.
These reduced dimensionality data improve the classification performance and reduce the classification time.
In order to achieve this, we first reduce the classification of NHRI and NVRI from five categories to two.
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The algorithm developed removed a large number of features from the original data sets without reducing the classification accuracy of the committees.
The problematic is how to find the relevant bands to classify the pixels of hyperspectral image without reducing the classification accuracy rate.
A reasonable way of alleviating this problem is to extract a small representative subset from the original dataset without reducing the classification accuracy.
The objective function E(W) reduces the classification error.
The score density plots (Figure 11) indicate the proposed method effectively reduces the overlap between the genuine and the impostor score distributions, which also reduces the classification errors.
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