Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

Abstract

Artificial bee colony algorithm is an effective algorithm for parameter optimization, but the traditional artificial bee colony algorithm is liable to fall into local extreme points at a later stage. In this paper, we propose an improved artificial bee colony optimization algorithm, which solves the problems of premature convergence and falling into the local extreme value in the classification of hyperspectral images. First we use an improved chaotic sequence with higher randomness to initialize and update nectar sources to expand the distribution of nectar sources. Secondly, the optimized adaptive step size is introduced into the neighborhood search to speed up the algorithm convergence and improve the search efficiency. Then we build an improved artificial bee colony algorithm support vector machine optimization model to mine the optimal values of penalty factor C and kernel function parameter σ. Next, the model was used to perform classification experiments on two hyperspectral images (University of Pavia, Indian Pine) with different attributes, and compared with the traditional bee colony algorithm, genetic algorithm, and particle swarm algorithm. Experimental results on HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both classification accuracy and convergence speed. INDEX TERMS Artificial bee colony, support vector machine, hyperspectral image, chaotic sequence.

Color Correction and Repair of Haze Images Under Hue-saturation-intensity Color Space and Machine Learning

“…Based on formula (9), formula (10), formula (11) and formula (1), the image degradation model that can obtain the HSI color space is:…”

“…Therefore, an accurate estimation of the transmittance and atmospheric light of the haze image is the key to improving the dehazing effect. The dark primary color a priori algorithm is to obtain the transmittance and atmospheric light of the image by comparing the original image and the haze image, which has strong limitations.In order to solve this problem, a machine learning algorithm is proposed to capture the features of haze images and estimate the transmittance of haze images more accurately.This article first uses the HIS color space method to construct the haze image-transmittance graph library [8]; Secondly, the machine learning algorithm is constructed using the k-means clustering algorithm optimized by the density parameter method and the GA-SVM algorithm of the support vector machine improved by the genetic algorithm [9][10][11]. The machine learning algorithm is used to train the haze transmittance image library to obtain visual words with different characteristics, and use these visual words with different characteristics to construct a visual dictionary.…”

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

“…While many studies address ensembles of weak classifiers in the RS ensemble, studies of strong classifiers are lacking. Research shows that the combination of strong classifiers with the RS algorithm, especially integration with an SVM [38], can improve the accuracy and reduce bias and the variance of classifiers [39][40][41]. In this paper, we select the SVM model as the base learner and use the RS algorithm to combine SVM classifiers.…”

Optimized SVM-Driven Multi-Class Approach by Improved ABC to Estimating Ship Systems State

“…If the initial solutions are not reasonable, they will have a great influence on the optimization performance of the whole algorithm. Therefore, to increase the diversity of the initial populations, make the initial solutions distribute in the solution space evenly, an unlearning improvement strategy [28], [29] will be adopted as follows:…”

“…where x i represents a support vector when the Lagrange multiplier α i >0. According to (29), (31): if α i <C, then μ i >0, and ξ i =0, that is, the sample is just on the boundary of the maximum margin; if α i =C, then μ i =0, so further if ξ i ≤1, the sample will fall within the maximum margin, and if ξ i >1, the sample will be misclassified. It can be seen that the model of soft margin SVM is only related to the support vectors, so the decision model can be expressed as same as (25).…”