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Content introduction
Introduction: In today’s information age, data prediction and analysis have become important tasks in various fields. Support vector machine (SVM), as a powerful machine learning algorithm, has been widely used in various fields. However, for multi-input and single-output data prediction problems, the traditional SVM algorithm faces challenges when processing high-dimensional data. In order to solve this problem, this study combines the artificial bee colony algorithm with SVM and proposes a multi-input single-output data prediction algorithm based on the artificial bee colony algorithm optimized support vector machine.
Artificial Bee Colony Algorithm: The Artificial Bee Colony Algorithm is a bionic optimization algorithm inspired by the behavior of bee colonies. Bee colonies are able to find the best food sources through information exchange and collective collaboration. In the artificial bee colony algorithm, bees represent the solution in the search space. By simulating the search behavior of bees, the global optimal solution can be found.
Support Vector Machine (SVM): Support vector machine is a supervised learning algorithm mainly used for classification and regression problems. The basic idea is to separate samples of different categories by constructing a hyperplane. SVM can find the optimal hyperplane by maximizing the classification boundary and minimizing the classification error. However, for multi-input single-output data prediction problems, the traditional SVM algorithm faces the problems of high computational complexity and poor generalization performance when processing high-dimensional data.
Multi-input single-output data prediction algorithm based on artificial bee colony algorithm optimizing support vector machine: This study proposes a multi-input single-output data prediction algorithm based on artificial bee colony algorithm optimizing support vector machine. First, feature extraction and preprocessing are performed on the multi-input single-output data set to be predicted. Then, the artificial bee colony algorithm is used to optimize the hyperparameters of the support vector machine to improve the generalization performance of the model. Finally, the new input data is predicted through the trained optimization model.
Experimental results: In order to verify the effectiveness of this algorithm, we conducted experiments using multiple real-world data sets. Experimental results show that compared with the traditional SVM algorithm, the support vector machine optimized based on the artificial bee colony algorithm has better performance in multi-input single-output data prediction problems. This algorithm can significantly improve the prediction accuracy of the model and has better generalization performance.
Conclusion: This study proposes a multi-input single-output data prediction algorithm based on artificial bee colony algorithm optimizing support vector machine by combining artificial bee colony algorithm with support vector machine. Experimental results show that this algorithm can significantly improve the prediction accuracy and has better generalization performance. Future research can further explore the application of the artificial bee colony algorithm in other machine learning algorithms and further optimize the performance of the algorithm.
Part of the code
%% Clear environment variables</code><code>warning off % Close alarm information</code><code>close all % Close open figure window</code><code>clear % Clear variables</code><code>clc % clear command line</code><code>?</code><code>%% import data</code><code>res = xlsread('dataset.xlsx');</code><code>?</code><code>%% divide the training set and test set</code><code>temp = randperm(357);</code><code>?</code><code>P_train = res(temp(1: 240), 1: 12)';</code><code>T_train = res(temp(1: 240), 13)';</code><code>M = size(P_train , 2);</code><code>?</code><code>P_test = res(temp(241: end), 1: 12)';</code><code>T_test = res(temp(241 : end), 13)';</code><code>N = size(P_test, 2);</code><code>?</code><code>%% data normalization</code><code>[p_train, ps_input] = mapminmax(P_train, 0, 1);</code><code>p_test = mapminmax('apply', P_test, ps_input);</code><code>t_train = ind2vec(T_train) ;</code><code>t_test = ind2vec(T_test );
Operation results
References
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[2] Zhu Zhijie, Zhang Hongwei, Wang Chunming. Prediction of stope floor damage depth based on artificial bee colony algorithm optimizing support vector machine [J]. Journal of Chongqing University: Natural Science Edition, 2015, 38(6):7.DOI:10.11835/ j.issn.1000-582X.2015.06.006.
[3] Li Wei. Research on data-driven redox potential soft measurement technology[D]. Xinjiang University[2023-10-06].DOI:CNKI:CDMD:2.1015.800756.