Method for constructing prediction model based on colibacillus algorithm

Abstract

The invention provides a method for constructing a prediction model based on a colibacillus algorithm. The method comprises the following steps: acquiring sample data and normalizing the acquired sample data; optimizing a penalty factor C and a kernel width gamma of a support vector machine by utilizing a colibacillus-based algorithm; and on the basis of the obtained penalty factor C and kernel width gamma, constructing a prediction model by utilizing the normalized data, and classifying and predicting a to-be-classified sample on the basis of the constructed prediction model. By implementingthe method, the penalty factor and the kernel width of the SVM are optimized on the basis of the colibacillus algorithm, the convergence rate and the convergence precision of the algorithm and the capacity of the algorithm for escaping from the local optimal solution can be fully utilized, and a better global approximate optimal solution is found to obtain the SVM model with higher classificationprecision.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method for constructing a prediction model based on a slime mold algorithm. Background technique [0002] Support Vector Machine (SVM) is often used to build a predictive model to analyze data, and the two most commonly used parameter optimization methods of Support Vector Machine (SVM) include grid search and gradient descent. In the first parameter optimization method, grid search is an exhaustive search method, which generally divides the specified parameter space by setting reasonable upper and lower limits of intervals and interval steps, and then analyzes the parameters represented by each grid node. Parameters are combined for training and prediction, and a set of parameters with the highest value in these prediction results is used as the best parameter of the final SVM model. Although this method can guarantee the optimal parameter combination in a given parameter s...

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Problems solved by technology

In practical applications, if their values ​​are too large or too small, the generalization performance of SVM will deteriorate.

[0005] However, using the existing meta-heuristic search algorithm to deal with the SVM parameter optimization problem needs to further improve the convergence speed and convergence accuracy of the algorithm, and improve the ability of the algorithm to escape from the local optimal solution, so as to find a better global approximate optimal untie

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