Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Baligh Al-Helali"'
Publikováno v:
IEEE Transactions on Evolutionary Computation. 25:1049-1063
Lack of knowledge is a common consequence of data incompleteness when learning from real-world data. To deal with such a situation, this work utilises transfer learning to re-use knowledge from different (yet related) but complete domains. Due to its
Publikováno v:
Soft Computing. 25:5993-6012
Incompleteness is one of the problematic data quality challenges in real-world machine learning tasks. A large number of studies have been conducted for addressing this challenge. However, most of the existing studies focus on the classification task
Publikováno v:
IEEE Transactions on Cybernetics. :1
Publikováno v:
2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE).
Publikováno v:
CEC
Data incompleteness is a pervasive problem in symbolic regression, and machine learning in general. Unfortunately, most symbolic regression methods are only applicable when the given data is complete. One common approach to handling this situation is
Publikováno v:
SSCI
Symbolic regression via genetic programming is considered as a crucial machine learning tool for empirical modelling. However, in reality, it is common for real-world data sets to have some data quality problems such as noise, outliers, and missing v
Publikováno v:
SSCI
Data incompleteness is one of the serious challenges in symbolic regression particularly when learning from real-world data. To handle this situation, the imputation approach works by replacing the missing values with estimated predictions. One popul
Publikováno v:
CEC
Transfer learning has been considered a key solution for the problem of learning when there is a lack of knowledge in some target domains. Its idea is to benefit from the learning on different (but related in some way) domains that have adequate know
Publikováno v:
CEC
This paper presents a feature selection method that incorporates a sensitivity-based single feature importance measure in a context-based feature selection approach. The single-wise importance is based on the sensitivity of the learning performance w
Publikováno v:
GECCO
Nowadays, transfer learning has gained a rapid popularity in tasks with limited data available. While traditional learning limits the learning process to knowledge available in a specific (target) domain, transfer learning can use parts of knowledge