Information Sciences—Informatics and Computer Science: An International Journal
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
Network intrusion detection: Evaluating cluster, discriminant, and logit analysis
Information Sciences: an International Journal
Relevant attribute discovery in high dimensional data: application to breast cancer gene expressions
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
A new discriminant analysis approach under decision-theoretic rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
With the ability to deal with both numeric and nominal information, rough set theory (RST), which can express knowledge in a rule-based form, has been one of the most important techniques in data analysis. However, applications of rough set theory for analyzing electricity loads are not widely discussed. Thus, this investigation employs rough set theory to analyze electricity loads. Additionally, to reduce the time generating reducts by rough set theory, linear discriminant analysis (LDA) is used to generate a reduct for rough set model. Therefore, this study designs a hybrid discriminant analysis and rough set model (DARST) to provide decision rules representing relations in an electric load information system. In this investigation, nine condition factors and variations of electricity loads are employed to examine the feasibility of the hybrid model. Experimental results reveal that the proposed model can efficiently and accurately analyze the relation between condition variables and variations of electricity loads. Consequently, the proposed model is a promising alternative for developing an electric load information system and offers decision rules base for the utility management as well as operations staff.