Multi-label incremental learning applied to web page categorization

Multi-label problems are challenging because each instance may be associated with an unknown number of categories, and the relationship among the categories is not always known. A large amount of data is necessary to infer the required information regarding the categories, but these data are normally available only in small batches and distributed over a period of time. In this work, multi-label problems are tackled using an incremental neural network known as the evolving Probabilistic Neural Network (ePNN). This neural network is capable of continuous learning while maintaining a reduced architecture, so that it can always receive training data when available with no drastic growth of its structure. We carried out a series of experiments on web page data sets and compared the performance of ePNN to that of other multi-label categorizers. On average, ePNN outperformed the other categorizers in four out of five metrics used for evaluation, and the structure of ePNN was less complex than that of the other algorithms evaluated.