Automatic classification using supervised learning in a medical document filtering application
Document classifiers can play an intermediate role in multilevel filtering systems. The effectiveness of a classifier that uses supervised learning was analyzed in terms of its accuracy and ultimately its influence on filtering. The analysis was conducted in two phases. In the first phase, a multilayer feed-forward neural network was trained to classify medical documents in the area of cell biology. The accuracy of the supervised classifier was established by comparing its performance with a baseline system that uses human classification information. A relatively high degree of accuracy was achieved by the supervised method, however, classification accuracy varied across classes. In the second phase, to clarify the impact of this performance on filtering, different types of user profiles were created by grouping subsets of classes based on their individual classification accuracy rates. Then, a filtering system with the neural network integrated into it was used to filter the medical documents and this performance was compared with the filtering results achieved using the baseline system. The performance of the system using the neural network classifier was generally satisfactory and, as expected, the filtering performance varied with regard to the accuracy rates of classes.
supervised learning neural networks document classification information filtering