Document Categorization by Genetic Algorithms
Chih-Hung Liu, Cheng-Che Lu, Wei-Po Lee · 2000
Abstmct-Following the on-going advance of Internet technology nowadays, we can easily provide information to and retrieve information from the Internet. However, the problem of information overload has to be overcome. One of the main issues to be addressed for the information overload problem is document classification. In this paper, we present an evolutionary approach to automatically categrize documents into appropriate categories. Our approach deals with different categories of documents separately: it evolves a numerical list that consists of the corresponding weights of the feature words for each class of documents. The experimental results show that our approach can easily evolve the classifiers of numerical lists, and the evolved classifiers perform better than those constructed by the traditional knearest neighbors approach.