A webpage classification system using genetic algorithm.

摘要

Because of an exponential increase of data available on the web, an efficient and accurate method for classifying this huge amount of data is very essential for fast information retrieval. This thesis presents a genetic algorithm approach for hierarchical web page categorization. Each category is represented by a keyword set with associated weights. Weights are evolved by the genetic algorithms, which performs a good solution for optimizing the properties of categories. Similarity formula is applied to calculate the similarity between the test webpage and the test category. Our experimental target is Yahoo.com. All 34,314 web pages under 11 different level categories are collected for training and testing. The experimental results show that our approach is very promising. Our approach relies on the existence of good quality texts for training. More work should be undertaken in the future to find the higher quality training web pages, which should lead to a better classification results.