Multi-label Text Categorization Using VG-RAM Weightless Neural Networks

In automated multi-label text categorization, an automatic categorization system should output a category 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 evaluate the performance of VG-RAM WNN on the categorization of Web pages, and compare our results with that of the multi-label lazy learning approach ML-KNN, the boosting-style algorithm BOOSTEXTER, the multi-label decision tree ADTBOOST.MH, and the multi-label kernel method Rank-SVM. Our experimental comparative analysis shows that, on average, VG-RAM WNN either outperforms the other mentioned techniques or show similar categorization performance.