relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/2960/
title: o	A new text semi-supervised multi-label learning model based on using the label-feature relations
creator: Ha, Quang Thuy
creator: Pham, Thi Ngan
creator: Nguyen, Van Quang
creator: Nguyen, Minh Chau
creator: Pham, Thanh Huyen
creator: Nguyen, Tri Thanh
subject: Information Technology (IT)
description: Multi-label learning has become popular and omnipresent in many real-world problems, especially in text classification applications, in which an instance could belong to different classes simultaneously. Due to these label constraints, there are some challenges occurring in building multi-label data. Semi-supervised learning is one possible approach to exploit abundantly unlabeled data for enhancing the classification performance with a small labeled dataset. In this paper, we propose a solution to select the most influential label based on using the relations among the labels and features to a semi-supervised multi-label classification algorithm on texts. Experiments on two datasets of Vietnamese reviews and English emails of Enron show the positive effects of the proposal.
date: 2018-09-05
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Ha, Quang Thuy and Pham, Thi Ngan and Nguyen, Van Quang and Nguyen, Minh Chau and Pham, Thanh Huyen and Nguyen, Tri Thanh  (2018) o A new text semi-supervised multi-label learning model based on using the label-feature relations.  In: 10th International Conference on Computational Collective Intelligence (ICCCI 2018), 5-7 September 2018, Bristol, United Kingdom.