A New Input Representation for Multi-Label Text Classification
Rodrigo Alfaro, Héctor Allende · ASME Press eBooks · 2011
Automatic text classification is the task of assigning unseen documents to a predefined set of classes or categories. Text Representation for classification has been traditionally approached with tf.idf due to its simplicity and good performance. Multi-label automatic text classification has been traditionally tackled in the literature either by transforming the problem to apply binary techniques or by adapting binary algorithms to work with multiple labels. We present tf.rfl, a novel text representation for the multi-label classification approach. Our proposal focuses on modifying the data set input to the algorithm, differentiating the input by the label to evaluate. Performance of...