TY - JOUR
T1 - Rules Determination Based on Time-Series Data to Classify Unsupervised Cases Based on Fuzzy Expert System
AU - Muqtadiroh, Feby Artwodini
AU - Usagawa, Tsuyoshi
AU - Rachmayanti, Riris Diana
AU - Nugroho, Supeno Mardi Susiki
AU - Yuniarno, Eko Mulyanto
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.
PY - 2023
Y1 - 2023
N2 - Classification of uncertain conditions requires computational modeling to obtain exact non-vague results for making the right decision, such as opening and closing school cases during a pandemic. We cannot rely solely on normative and textual government regulations because of numerous constraints and uncertainty in implementation. Unsupervised classification techniques can deal with such issues without needing prior references that contain definitive hesitancy. This motivates us to use a fuzzy system based on knowledge-based composition rules for complex problems such as the dynamics of COVID-19 because of its ability to adapt to changes and uncertainties. Therefore, we construct rules based on knowledge about COVID-19 to the issue of opening/closing schools using three fuzzy approaches: conventional fuzzy, intuitionistic fuzzy system (IFS), and fuzzy c-means (FCM). We can demonstrate a correlation between the number of school openings and the COVID-19 dynamics by utilizing the fuzzy approach to reduce the degree of hesitance. Experiments on available public time-series datasets demonstrate that the IFS is more efficient in forming rigidly distinct two classes. The results indicate that the accuracy of IFS is 99.47%, FCM is 91.28, and conventional FS is 84.33%, including the IFS silhouette score, which is higher than the others, at 0.91 or closer to 1, indicating excellent classification results. IFS is less superior in running time, while FCM is the fastest.
AB - Classification of uncertain conditions requires computational modeling to obtain exact non-vague results for making the right decision, such as opening and closing school cases during a pandemic. We cannot rely solely on normative and textual government regulations because of numerous constraints and uncertainty in implementation. Unsupervised classification techniques can deal with such issues without needing prior references that contain definitive hesitancy. This motivates us to use a fuzzy system based on knowledge-based composition rules for complex problems such as the dynamics of COVID-19 because of its ability to adapt to changes and uncertainties. Therefore, we construct rules based on knowledge about COVID-19 to the issue of opening/closing schools using three fuzzy approaches: conventional fuzzy, intuitionistic fuzzy system (IFS), and fuzzy c-means (FCM). We can demonstrate a correlation between the number of school openings and the COVID-19 dynamics by utilizing the fuzzy approach to reduce the degree of hesitance. Experiments on available public time-series datasets demonstrate that the IFS is more efficient in forming rigidly distinct two classes. The results indicate that the accuracy of IFS is 99.47%, FCM is 91.28, and conventional FS is 84.33%, including the IFS silhouette score, which is higher than the others, at 0.91 or closer to 1, indicating excellent classification results. IFS is less superior in running time, while FCM is the fastest.
KW - Classification
KW - Fuzzy system
KW - Hesitant degree
KW - Rules determination
KW - Time-series data
KW - Unsupervised
UR - http://www.scopus.com/inward/record.url?scp=85158105912&partnerID=8YFLogxK
U2 - 10.22266/ijies2023.0630.20
DO - 10.22266/ijies2023.0630.20
M3 - Article
AN - SCOPUS:85158105912
SN - 2185-310X
VL - 16
SP - 258
EP - 268
JO - International Journal of Intelligent Engineering and Systems
JF - International Journal of Intelligent Engineering and Systems
IS - 3
ER -