IMPROVED EXTREME LEARNING MACHINE METHOD BASED ON ARTIFICIAL BEE COLONY OPTIMIZATION

摘要

The present invention discloses an improved extreme learning machine method based on artificial bee colony optimization, which includes the following steps: Step 1, generating an initial solution for SN individuals: Step 2, globally optimizing a connection weight ω and a threshold b for the extreme learning machine; Step 3, locally optimizing the connection weight ω and threshold b of the extreme learning machine; Step 4, if food source information is not updated within a certain time, transforming employed bees into scout bees, and reinitializing the individuals after returning to Step 1; and Step 5, extracting the connection weight ω and threshold b of the extreme learning machine from the best individuals, and verifying by using a test set. With the method provided by the present invention, the defect of worse results of the traditional extreme learning machine in classification and regression is overcomed, and effectively improves the results of classification and regression.