TY - GEN
T1 - An improved text feature selection for clustering using binary grey wolf optimizer
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:
© Springer Nature Singapore Pte Ltd 2021.
PY - 2021
Y1 - 2021
N2 - Text Feature Selection (FS) is a significant step in text clustering (TC). Machine learning applications eliminate unnecessary features in order to enhance learning effectiveness. This work proposes a binary grey wolf optimizer (BGWO) algorithm to tackle the text FS problem. This method introduces a new implementation of the GWO algorithm by selecting informative features from the text. These informative features are evaluated using the clustering technique (i.e., k-means) so that time complexity is reduced, and the clustering algorithm’s efficiency is improved. The performance of BGWO is examined on six published datasets, including Tr41, Tr12, Wap, Classic4, 20Newsgroups, and CSTR. The results showed that the BGWO output outperformed the rest of the compared algorithms such as GA and BPSO based on the measurements of the evaluation. The experiments also showed that the BGWO method could achieve an average purity of 46.29%, F-measure of 42.23%.
AB - Text Feature Selection (FS) is a significant step in text clustering (TC). Machine learning applications eliminate unnecessary features in order to enhance learning effectiveness. This work proposes a binary grey wolf optimizer (BGWO) algorithm to tackle the text FS problem. This method introduces a new implementation of the GWO algorithm by selecting informative features from the text. These informative features are evaluated using the clustering technique (i.e., k-means) so that time complexity is reduced, and the clustering algorithm’s efficiency is improved. The performance of BGWO is examined on six published datasets, including Tr41, Tr12, Wap, Classic4, 20Newsgroups, and CSTR. The results showed that the BGWO output outperformed the rest of the compared algorithms such as GA and BPSO based on the measurements of the evaluation. The experiments also showed that the BGWO method could achieve an average purity of 46.29%, F-measure of 42.23%.
KW - Binary grey wolf optimizer
KW - K-means
KW - Text clustering
KW - Text feature selection problem
KW - Text mining
UR - https://www.scopus.com/pages/publications/85088536055
U2 - 10.1007/978-981-15-5281-6_34
DO - 10.1007/978-981-15-5281-6_34
M3 - Conference contribution
AN - SCOPUS:85088536055
SN - 9789811552809
T3 - Lecture Notes in Electrical Engineering
SP - 503
EP - 516
BT - Proceedings of the 11th National Technical Seminar on Unmanned System Technology, NUSYS 2019
A2 - Md Zain, Zainah
A2 - Ahmad, Hamzah
A2 - Pebrianti, Dwi
A2 - Mustafa, Mahfuzah
A2 - Abdullah, Nor Rul Hasma
A2 - Samad, Rosdiyana
A2 - Mat Noh, Maziyah
PB - Springer
T2 - 11th National Technical Symposium on Unmanned System Technology, NUSYS 2019
Y2 - 2 December 2019 through 3 December 2019
ER -