Incremental learning with partial-supervision based on hierarchical Dirichlet process and the application for document classification

• Published in: Applied soft computing Vol. 33; pp. 250 - 262
• Main Authors: Wang, Di, Al-Rubaie, Ahmad
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-08-2015
• Subjects: Hierachical Dirichlet process; Natural language processing; Partial-supervision incremental learning; Semi-supervised learning; Text classification; Partial-supervision incremental learning; Natural language processing; Hierachical Dirichlet process; Semi-supervised learning; Text classification
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2015.04.044

•Partial-supervision: use available knowledge to guide model learning process for better accuracy.•Incremental learning: adjust parameters and model structure to the latest information.•Introduce granular computing idea to achieve better accuracy and detect new emergent categories.
Hierarchical Dirichlet process (HDP) is an unsupervised method which has been widely used for topic extraction and document clustering problems. One advantage of HDP is that it has an inherent mechanism to determine the total number of clusters/topics. However, HDP has three weaknesses: (1) there is no mechanism to use known labels or incorporate expert knowledge into the learning procedure, thus precluding users from directing the learning and making the final results incomprehensible; (2) it cannot detect the categories expected by applications without expert guidance; (3) it does not automatically adjust the model parameters and structure in a changing environment. To address these weaknesses, this paper proposes an incremental learning method, with partial supervision for HDP, which enables the topic model (initially guided by partial knowledge) to incrementally adapt to the latest available information. An important contribution of this work is the application of granular computing to HDP for partial-supervision and incremental learning which results in a more controllable and interpretable model structure. These enhancements provide a more flexible approach with expert guidance for the model learning and hence results in better prediction accuracy and interpretability.