Automated multi-label text categorization with VG-RAM weightless neural networks

In automated multi-label text categorization, an automatic categorization system should output a label set, whose size is unknown a priori, for each document under analysis. Many machine learning techniques have been used for building such automatic text categorization systems. In this paper, we examine virtual generalizing random access memory weightless neural networks (VG-RAM WNN), an effective machine learning technique which offers simple implementation and fast training and test, as a tool for building automatic multi-label text categorization systems. We evaluated the performance of VG-RAM WNN on two real-world problems:, (i) categorization of free-text descriptions of economic activities and (ii) categorization of Web pages, and compared our results with that of the multi-label lazy learning approach (Multi-Label K-Nearest Neighbors, ML-KNN). Our experimental comparative analysis showed that, on average, VG-RAM WNN either outperforms ML-KNN or show similar categorization performance.