TY - GEN
T1 - Optimized Feature Selection Approach Based on Entropy for Multi-Class Data Classification
AU - Krisyesika, Krisyesika
AU - Buliali, Joko Lianto
AU - Saikhu, Ahmad
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Efficient machine learning heavily relies on solving the critical issue of feature selection, which serves as a valuable pre-processing method for improving data quality. Classification models face significant challenges, particularly when confronted with data containing irrelevant and redundant features. Unfortunately, only a few techniques specifically address feature selection in datasets with multi-class attributes. This research paper introduces a novel approach for feature selection called TSEFS, which utilizes entropy-based techniques such as fuzzy entropy and mutual information. TSEFS employs a two-stage process to select relevant features. The experimental results on three datasets demonstrate that the proposed method outperformed the existing feature selection methods.
AB - Efficient machine learning heavily relies on solving the critical issue of feature selection, which serves as a valuable pre-processing method for improving data quality. Classification models face significant challenges, particularly when confronted with data containing irrelevant and redundant features. Unfortunately, only a few techniques specifically address feature selection in datasets with multi-class attributes. This research paper introduces a novel approach for feature selection called TSEFS, which utilizes entropy-based techniques such as fuzzy entropy and mutual information. TSEFS employs a two-stage process to select relevant features. The experimental results on three datasets demonstrate that the proposed method outperformed the existing feature selection methods.
KW - feature selection
KW - fuzzy entropy
KW - information theory
KW - multi-class classification
KW - mutual information
UR - http://www.scopus.com/inward/record.url?scp=85178069423&partnerID=8YFLogxK
U2 - 10.1109/EECSI59885.2023.10295582
DO - 10.1109/EECSI59885.2023.10295582
M3 - Conference contribution
AN - SCOPUS:85178069423
T3 - International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
SP - 349
EP - 354
BT - Proceeding - EECSI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2023
Y2 - 20 September 2023 through 21 September 2023
ER -