eprintid: 2960
rev_number: 10
eprint_status: archive
userid: 286
dir: disk0/00/00/29/60
datestamp: 2018-06-07 07:04:09
lastmod: 2018-06-07 07:04:09
status_changed: 2018-06-07 07:04:09
type: conference_item
metadata_visibility: show
creators_name: Ha, Quang Thuy
creators_name: Pham, Thi Ngan
creators_name: Nguyen, Van Quang
creators_name: Nguyen, Minh Chau
creators_name: Pham, Thanh Huyen
creators_name: Nguyen, Tri Thanh
creators_id: thuyhq@vnu.edu.vn
creators_id: nganpt.di12@vnu.edu.vn
creators_id: ntthanh@vnu.edu.vn
title: o	A new text semi-supervised multi-label learning model based on using the label-feature relations
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: 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
date_type: published
full_text_status: none
pres_type: paper
event_title: 10th International Conference on Computational Collective Intelligence (ICCCI 2018)
event_location: Bristol, United Kingdom
event_dates: 5-7 September 2018
event_type: conference
refereed: TRUE
citation:   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.