2 https://github.com/oulenz/fuzzy-rough-learn. fuzzy-rough-learn 0.1 493 3 Contents fuzzy-rough-learn implements three of the fuzzy rough set algorithms mentioned in Sect. 1: FRFS, FRPS and FRNN, making them available in Python for the first time. In addition, we have included two ...
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 fuzzy rough set theory, feature selection is the most important aspect due to its performance...
Fuzzy rough set theoryInformation entropyMixed dataFeature selection in the data with different types of feature values, i.e., the heterogeneous or mixed ... Z Xiao,C Mei,D Chen,... - 《Pattern Recognition》 被引量: 51发表: 2016年 Fuzzy Entropy-Assisted Fuzzy-Rough Feature Selection Feature...
In recent years, the increasing interest in fuzzy rough set theory has allowed the definition of novel accurate methods for feature selection. Although their stand-alone application can lead to the construction of high quality classifiers, they can be improved even more if other preprocessing techniqu...
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...
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 ...
Lenz, O.U.; Peralta, D.; Cornelis, C. Fuzzy-Rough-Learn 0.1: A Python Library for Machine Learning with Fuzzy Rough Sets. InRough Sets; Bello, R., Miao, D., Falcon, R., Nakata, M., Rosete, A., Ciucci, D., Eds.; Lecture Notes in Computer Science; Springer International Publis...
Enhancing evolutionary instance selection algorithms by means of fuzzy rough set based feature selection. Inf. Sci. 2012, 186, 73–92. [Google Scholar] [CrossRef] Garcia, S.; Derrac, J.; Cano, J.-R.; Herrera, F. Prototype Selection for Nearest Neighbor Classification: Taxonomy and ...
Moreover, two different feature selection methods are used which include fuzzy rough set quick reduction algorithm and a novel wrapper feature selection method. Finally, the multi-objective optimization algorithm NSGA-II (non-dominated sorting genetic algorithm II) is used to select the optimal set ...