Improving VG-RAM WNN Multi-label Text Categorization via Label Correlation

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

Download Full-text

Local positive and negative label correlation analysis with label awareness for multi-label classification

International Journal of Machine Learning and Cybernetics ◽

10.1007/s13042-021-01352-2 ◽

2021 ◽

Author(s):

Rui Huang ◽

Liuyue Kang

Keyword(s):

Correlation Analysis ◽

Label Correlation

Download Full-text

Attention-based hierarchical recurrent neural networks for MOOC forum posts analysis

Journal of Ambient Intelligence and Humanized Computing ◽

10.1007/s12652-020-02747-9 ◽

2020 ◽

Author(s):

Nicola Capuano ◽

Santi Caballé ◽

Jordi Conesa ◽

Antonio Greco

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Text Categorization ◽

Online Courses ◽

Recurrent Network ◽

Subject Area ◽

Conversational Agents ◽

Collaborative Tools ◽

Experimental Findings ◽

Productive Talk

AbstractMassive open online courses (MOOCs) allow students and instructors to discuss through messages posted on a forum. However, the instructors should limit their interaction to the most critical tasks during MOOC delivery so, teacher-led scaffolding activities, such as forum-based support, can be very limited, even impossible in such environments. In addition, students who try to clarify the concepts through such collaborative tools could not receive useful answers, and the lack of interactivity may cause a permanent abandonment of the course. The purpose of this paper is to report the experimental findings obtained evaluating the performance of a text categorization tool capable of detecting the intent, the subject area, the domain topics, the sentiment polarity, and the level of confusion and urgency of a forum post, so that the result may be exploited by instructors to carefully plan their interventions. The proposed approach is based on the application of attention-based hierarchical recurrent neural networks, in which both a recurrent network for word encoding and an attention mechanism for word aggregation at sentence and document levels are used before classification. The integration of the developed classifier inside an existing tool for conversational agents, based on the academically productive talk framework, is also presented as well as the accuracy of the proposed method in the classification of forum posts.

Download Full-text

MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3451392 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-24

Author(s):

Yaojin Lin ◽

Qinghua Hu ◽

Jinghua Liu ◽

Xingquan Zhu ◽

Xindong Wu

Keyword(s):

Empirical Studies ◽

Feature Space ◽

Training Data ◽

Data Sets ◽

Learning Framework ◽

Feature Spaces ◽

Public Data ◽

Margin Distribution ◽

Label Correlations ◽

Label Correlation

In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm, mu lti- l abel-specific f eature space e nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label’s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.

Download Full-text