The classification of textual data often yields important information. Most
classifiers work in a closed world setting where the classifier is trained on a
known corpus, and then it is tested on unseen examples that belong to one of
the classes seen during training. Despite the usefulness of this design, often
there is a need to classify unseen examples that do not belong to any of the
classes on which the classifier was trained. This paper describes the open set
scenario where unseen examples from previously unseen classes are handled while
testing. This further examines a process of enhanced open set classification
with a deep neural network that discovers new classes by clustering the
examples identified as belonging to unknown classes, followed by a process of
retraining the classifier with newly recognized classes. Through this process
the model moves to an incremental learning model where it continuously finds
and learns from novel classes of data that have been identified automatically.
This paper also develops a new metric that measures multiple attributes of
clustering open set data. Multiple experiments across two author attribution
data sets demonstrate the creation an incremental model that produces excellent
results.

本文介绍了一种使用深度神经网络的增强开放式分类过程，通过对无类别数据进行聚类识别新类别并重新训练分类器从而将模型转移到增量学习模型。多次实验均表明该模型的优秀性能。