TY - GEN
T1 - A Text Feature Selection Technique based on Binary Multi-Verse Optimizer for Text Clustering
AU - Abasi, Ammar Kamal
AU - Khader, Ahamad Tajudin
AU - Al-Betar, Mohammed Azmi
AU - Naim, Syibrah
AU - Makhadmeh, Sharif Naser
AU - Alyasseri, Zaid Abdi Alkareem
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5/16
Y1 - 2019/5/16
N2 - Feature selection is regarded as an important task in data mining. The applications of machine learning eliminate irrelevantly, redundant features so that the learning performance is improved. A novel feature selection method for unsupervised text clustering, that is, binary multi-verse optimizer algorithm (BMVO) is proposed in this paper. A new application of the MVO algorithm is introduced via this method, which selects important text features. Then, these important features are tested using a k-means clustering algorithm to enhance performance and lessen the cost of the proposed algorithm computational time. The BMVO performance is examined on 6 datasets that are published including Classic4, Wap, tr41, tr12, 20Newsgroups, and CSTR. Based on the measures of the evaluation, the obtained results showed that the BMVO performance has outperformed the rest of the comparative algorithms.
AB - Feature selection is regarded as an important task in data mining. The applications of machine learning eliminate irrelevantly, redundant features so that the learning performance is improved. A novel feature selection method for unsupervised text clustering, that is, binary multi-verse optimizer algorithm (BMVO) is proposed in this paper. A new application of the MVO algorithm is introduced via this method, which selects important text features. Then, these important features are tested using a k-means clustering algorithm to enhance performance and lessen the cost of the proposed algorithm computational time. The BMVO performance is examined on 6 datasets that are published including Classic4, Wap, tr41, tr12, 20Newsgroups, and CSTR. Based on the measures of the evaluation, the obtained results showed that the BMVO performance has outperformed the rest of the comparative algorithms.
KW - Binary multiverse optimizer
KW - K-means
KW - Text Clustering
KW - Text Feature Selection Problem
KW - Text mining
UR - https://www.scopus.com/pages/publications/85067133147
U2 - 10.1109/JEEIT.2019.8717491
DO - 10.1109/JEEIT.2019.8717491
M3 - Conference contribution
AN - SCOPUS:85067133147
T3 - 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2019 - Proceedings
SP - 1
EP - 6
BT - 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2019
Y2 - 9 April 2019 through 11 April 2019
ER -