Evolving Recurrent Linear-GP for Document Classification and Word Tracking

In this paper, we propose a novel document classification system where the recurrent linear Genetic Programming is employed to classify the documents that are represented in encoded word sequences. During this process, word sequences of documents are tracked, frequent patterns are detected and document is classified. We describe the word encoding model and the recurrent linear Genetic Programming based classification mechanism. The performance results on benchmark data set Reuters 21578 show that this system can analyze the temporal sequence patterns of a document and get competitive performance on classification. We expect that it can be easily applied to other application areas, where the temporal sequences are very significant.