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Content introduction
In the field of machine learning, Support Vector Machine (SVM) is a very commonly used and effective algorithm that can be used for classification and regression problems. This article will focus on the algorithm steps of SVM regression prediction research based on five-fold cross-validation.
Step 1: Data preparation First, we need to prepare the data set for regression prediction. This data set should contain information about the target variable to be predicted and a set of related feature variables. Ensure that there are no missing values in the data set and perform necessary data cleaning and preprocessing.
Step 2: Data partition: Divide the data set into a training set and a test set. Typically, we use 80% of the data set as the training set and the remaining 20% as the test set. This ensures that we use enough data when training the model and have enough data for validation when testing the model.
Step 3: Standardize features Since SVM is very sensitive to the scale of features, we need to standardize the features before training the model. This can be accomplished by subtracting the value of each feature from its mean and dividing by its standard deviation. This ensures that all features have similar scales.
Step 4: Select the kernel function. SVM uses the kernel function in the regression problem to map the input features into a high-dimensional space. Choosing an appropriate kernel function is crucial to the performance of the model. Commonly used kernel functions include linear kernel functions, polynomial kernel functions and radial basis kernel functions. Choose an appropriate kernel function based on the characteristics of the problem and the characteristics of the data set.
Step 5: Train the model. Use the training set to train the SVM model. During the training process, SVM will find an optimal hyperplane to maximize the interval between samples and classify the samples correctly. This hyperplane will be used to predict new unseen data.
Step 6: Model evaluation Use the test set to evaluate the trained model. Evaluate the performance of the model by calculating the model’s prediction error indicators on the test set, such as Root Mean Squared Error (RMSE) or Mean Absolute Error (MAE). These metrics can help us judge the accuracy and predictive power of the model.
Step 7: Parameter tuning If the performance of the model is not ideal, we can improve its performance by adjusting the parameters of the SVM model. Common parameters include regularization parameter C, kernel function parameters, penalty term parameters, etc. By trying different parameter combinations, we can find the optimal parameter settings that improve the model’s predictive performance.
Step 8: Model Application Once we obtain an SVM regression model that performs well on the test set, we can apply it to new unknown data for prediction and inference. This can help us make decisions about future events or unknown situations.
Summary: The research algorithm steps of support vector machine (SVM) regression prediction based on five-fold cross-validation include data preparation, data partitioning, standardized features, selection of kernel function, training model, model evaluation, parameter tuning and model application. By following these steps, we can build an accurate and reliable SVM regression model for solving various regression problems.
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
[1] Qi Lin. Short-term traffic flow prediction and system implementation based on support vector machine regression [D]. Northeastern University, 2013.
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1 Improvements and applications of various intelligent optimization algorithms
Production scheduling, economic scheduling, assembly line scheduling, charging optimization, workshop scheduling, departure optimization, reservoir scheduling, three-dimensional packing, logistics location selection, cargo space optimization, bus scheduling optimization, charging pile layout optimization, workshop layout optimization, Container ship stowage optimization, water pump combination optimization, medical resource allocation optimization, facility layout optimization, visible area base station and drone site selection optimization
2 Machine learning and deep learning
Convolutional neural network (CNN), LSTM, support vector machine (SVM), least squares support vector machine (LSSVM), extreme learning machine (ELM), kernel extreme learning machine (KELM), BP, RBF, width Learning, DBN, RF, RBF, DELM, XGBOOST, TCN realize wind power prediction, photovoltaic prediction, battery life prediction, radiation source identification, traffic flow prediction, load prediction, stock price prediction, PM2.5 concentration prediction, battery health status prediction, water body Optical parameter inversion, NLOS signal identification, accurate subway parking prediction, transformer fault diagnosis
2. Image processing
Image recognition, image segmentation, image detection, image hiding, image registration, image splicing, image fusion, image enhancement, image compressed sensing
3 Path planning
Traveling salesman problem (TSP), vehicle routing problem (VRP, MVRP, CVRP, VRPTW, etc.), UAV three-dimensional path planning, UAV collaboration, UAV formation, robot path planning, raster map path planning , multimodal transportation problems, vehicle collaborative UAV path planning, antenna linear array distribution optimization, workshop layout optimization
4 UAV applications
UAV path planning, UAV control, UAV formation, UAV collaboration, UAV task allocation, and online optimization of UAV safe communication trajectories
5 Wireless sensor positioning and layout
Sensor deployment optimization, communication protocol optimization, routing optimization, target positioning optimization, Dv-Hop positioning optimization, Leach protocol optimization, WSN coverage optimization, multicast optimization, RSSI positioning optimization
6 Signal processing
Signal recognition, signal encryption, signal denoising, signal enhancement, radar signal processing, signal watermark embedding and extraction, EMG signal, EEG signal, signal timing optimization
7 Power system aspects
Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration
8 Cellular Automata
Traffic flow, crowd evacuation, virus spread, crystal growth
9 Radar aspect
Kalman filter tracking, track correlation, track fusion
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