Web classification based on improved quantum-behaved particle swarm optimization and support vector machine

With the emergence of the vast amounts of information on the web pages,web page classification has become an important research direction for the field of data mining,it is important technology for a fast and effective use of vast amounts of information on the web pages.In order to overcome slow rate of convergence and low accuracy of web page classification based on support vector machine,the improved quantum-behaved particle swarm optimization and support vector machine was combineal,and a web classification method was present based on improved quantum-behaved particle swarm optimization and support vector machine.First,cauchy distribution was used to improve the quantum particle swarm algorithm.Secondly,the improved quantum particle swarm optimization algorithm was used to optimize the parameter of support vector machine.Finally,the support vector machine was used to classify the web pages.The experimental results show that the method has high accuracy,recall and F1-Measure,and also improve the efficiency of web page classification.