Multi label text classification for un-trained data through supervised learning
Publication Type
Conference Proceeding Article
Publication Date
6-2017
Abstract
World of digital data is growing at an aggressive rate, where every single minute new data is created and processed. All information retrieval processes are gone from insufficient to overflowing. It is doubling each and every year which makes information retrieval more challenging. Our focus is to turn this immense data streams from a liability to our strengths.
Keywords
Multi label classification, supervised learning, un-trained data, lexion, MITB student
Discipline
Databases and Information Systems | Data Science | Numerical Analysis and Scientific Computing
Publication
2017 International Conference on Intelligent Computing and Control (I2C2): Coimbatore, India, June 23-24: Proceedings
First Page
1
Last Page
3
ISBN
9781538603741
Identifier
10.1109/I2C2.2017.8321804
Publisher
IEEE
City or Country
Piscataway, NJ
Embargo Period
6-8-2021
Citation
BUDHIRAJA, Mayank.
Multi label text classification for un-trained data through supervised learning. (2017). 2017 International Conference on Intelligent Computing and Control (I2C2): Coimbatore, India, June 23-24: Proceedings. 1-3.
Available at: https://ink.library.smu.edu.sg/sis_research/5991
Additional URL
https://doi.org/10.1109/I2C2.2017.8321804
Comments
Code available at https://github.com/mayank-budhiraja/MultiLabel-text-classification-using-supervised-learning