Heterogeneous learner for Web page classification

Classification of an interesting class of Web pages has been an interesting problem. Typical machine learning algorithms for this problem require two classes of data for training: positive and negative training examples. However in application to Web page classification, gathering an unbiased sample of negative examples appears to be difficult. We propose a heterogeneous learning framework for classifying Web pages, which (1) eliminates the need for negative training data, and (2) increases classification accuracy by using two heterogeneous learners. Our framework uses two heterogeneous learners-a decision list and a linear separator which complement each other-to eliminate the need for negative training data in the training phase and to increase the accuracy in the testing phase. Our results show that our heterogeneous framework achieves high accuracy without requiring negative training data; it enhances the accuracy of linear separators by reducing the errors on "low-margin data". That is, it classifies more accurately while requiring less human efforts in training.