Two-dimensional Feature Selection Method Based on Whale Algorithm and Its Matlab Code Implementation
The problem of feature selection is of great significance in the field of machine learning, and its purpose is to select some of the most representative and relevant features from raw data for further analysis and processing. Whale algorithm is a new type of optimization algorithm, which has the characteristics of fast convergence speed and strong global search ability, and has been widely used in feature selection problems. This article will introduce a two-dimensional feature selection method based on the whale algorithm, and provide the corresponding Matlab code implementation.
1. Introduction to Whale Algorithm
Whale Optimization Algorithm (WOA) is an optimization algorithm based on biological evolution, which simulates the behavior of whales when they prey, and optimizes the objective function through continuous search and iteration. The basic process of the algorithm is as follows:
1. Initialize a population, including several individuals, and each individual represents a set of feasible solutions.
2. Calculate the fitness value of each individual, that is, the value of the objective function.
3. Update the positions and velocities of all individuals according to the current optimal individual.
4. For some individuals, perform some special operations (such as jetting, running) to increase the diversity and search capabilities of the algorithm.
5. According to a certain stopping criterion (such as reaching the maximum number of iterations, the value of the objective function meeting certain conditions, etc.), the algorithm is terminated and the optimal solution is output.
2. Two-dimensional feature selection method based on whale algorithm
In the two-dimensional feature selection problem, we need to select m from n features for classification, where n and m are both positive integers, and n>m. Here we use a feature selection method based on the degree of association, that is, by calculating the correlation coefficient between each feature and the classification result, to measure its influence on the classification result. Specifically, we can use the Pearson correlation coefficient to measure the linear correlation between two variables, and its calculation formula is: