Fuzzy rough set theory provides a better glimpse in order to handle vagueness and indiscernibility available in the dataset, which are the consequences of uncertainty in knowledge. Among all applications of fuzz
The fuzzy rough set theory has been widely used to deal with uncertainty in real-valued or even complex data, in which one of the most concerned issues is feature selection. Since a real-world data set generally contains redundant data objects (or instances) and errors which lead to the ...
If you use or refer to fuzzy-rough-learn in a scientific publication, please citethis paper: Lenz OU, Cornelis C, Peralta D (2022). fuzzy-rough-learn 0.2: a Python library for fuzzy rough set algorithms and one-class classification. FUZZ-IEEE 2022: Proceedings of the IEEE International Con...
Motivated by these two issues, we study incremental feature selection approaches based on a fuzzy dominance neighborhood rough set (FDNRS) for dynamic interval-valued ordered data in this work. First, we propose the FDNRS model for an interval-valued ordered decision system (IvODS) and ...
The effective use of user data to evaluate new concept designs is a more feasible approach than traditional user surveys. The specific steps we take to obtain information on user preferences are: first, use python’s requests library to crawl the user comment corpus data, and then use the Jie...
[200], the authors used an LSTM network with rough set theory [201] and interval type-2 fuzzy sets for short-term wind speed forecasting (dataset from Bandar-Abbas City, Iran), with the aid of mutual information approach for efficient variable input selection. A novel ultra-short-term ...
In [200], the authors used an LSTM network with rough set theory [201] and interval type-2 fuzzy sets for short-term wind speed forecasting (dataset from Bandar-Abbas City, Iran), with the aid of mutual information approach for efficient variable input selection. A novel ultra-short-term ...
fuzzy rough setevaluation functionsdependency functionfeature selectionfuzzy rough iterative computation modelTo deal with set-valued data, one usually exploits the information value similarity, which is fed back to the object set. Alternatively, it is feasible but scarcely considered to handle set-...
feature selection in a mixed data set. It is proved that the newly-defined entropy meets the common requirement of monotonicity and can equivalently characterize the existing attribute reductions in the fuzzy rough set theory. Then, a featureselection algorithmis formulated based on the proposed ...
A fuzzy rough set approach for incremental feature selection on hybrid information systems Fuzzy Sets and Systems Volume 258, 1 January 2015, Pages 39-60 Purchase options CorporateFor R&D professionals working in corporate organizations. Academic and personalFor academic or personal use only. Looking ...