What are rough neural network and rough genetic algorithm in pattern recognition?
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Rough neural network and rough genetic algorithm are techniques used in pattern recognition. Rough neural network combines rough set theory with neural networks to improve recognition performance and classify input images . It aims to find hidden patterns and regularities in data, especially when dealing with uncertain or vague knowledge. The rough neural network algorithm is integrated with grid features technique to achieve good results . On the other hand, rough genetic algorithm combines rough set theory with genetic algorithms to reduce the dimensionality of data and discover hidden patterns . It is effective in data mining and results in classifiers of better quality . The combination of rough sets with neural networks and genetic algorithms has shown promising results in pattern recognition tasks .
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16 Citations | The paper discusses the advantages of rough hybridization with neural networks in data mining, but it does not specifically mention rough neural networks or rough genetic algorithms in pattern recognition. |
| The paper does not provide information about rough neural networks or rough genetic algorithms in pattern recognition. | |
| The paper does not mention rough genetic algorithm. The paper is about a hybrid technique using rough neural network and rough set for signature recognition. | |
01 May 2017 5 Citations | The paper does not provide information about "rough neural network" or "rough genetic algorithm" in pattern recognition. |
| The provided paper does not mention anything about rough neural networks or rough genetic algorithms in pattern recognition. |
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How rough set is used for prediction?5 answersRough set is used for prediction by applying machine learning techniques and data analysis. It is used to reduce the dimensionality of data and remove redundant features, improving the accuracy and recall rate of prediction models. Rough set theory is used to calculate the core attribute and find the condition attribute with the strongest decision attribute dependence. This helps in constructing decision trees and avoiding over-fitting by using techniques like genetic algorithms and ten-fold cross validation. Neighborhood rough set is used to approximate data attributes and construct a rule base, which is then processed using fuzzy interpolation inference for prediction. This combination of rough set and fuzzy interpolation enables accurate prediction even with small samples and linguistic ambiguity.
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What are some specific applications of rough set theory in conjunction with other feature selection techniques?5 answersRough set theory has been applied in conjunction with other feature selection techniques in various applications. Plaugher proposed a class-specific feature selection approach using neighborhood rough sets, which improved classification performance without converting continuous data into discrete or fuzzy data. Rajeshwari and Kavitha used Fuzzy C-Means (FCM) - Enhanced Tolerance-based Intuitionistic Fuzzy Rough Set Theory (ETIFRST) for feature selection in arrhythmia classification, achieving high accuracy on MIT-BIH and CPCS 2018 datasets. Zhou et al. introduced an Early Terminated Online Streaming Feature Selection framework (OSFS-ET) based on rough set theory, which terminated feature selection early while maintaining excellent performance. Huda and Banka proposed three algorithms based on particle swarm optimization with fuzzy rough fitness function for optimal feature subset selection, outperforming traditional and other rough set-based approaches. Alia and Taweel developed a filter feature selection approach combining Binary Cuckoo Search (BCS) and rough set theory, achieving improved classification accuracy and minimizing the number of features.
What is\ rough neural network and rough genetic algorithms?3 answersA rough neural network is a combination of rough neurons and conventional neurons. It uses pairs of upper and lower bounds as values for input and output. This approach is useful in situations where prediction models need to use ranges as values for input and/or output variables. By integrating rough set theory with neural network theory, a novel information fusion method based on rough neural network has been proposed. This method is used to fuse different-source images, such as infrared and visible images, in agricultural robots. Experimental results have shown that the fusion effect and speed of the rough neural network are better than standard wavelet transform and conventional neural networks.
Rough genetic algorithms, on the other hand, are a hybrid data mining algorithm that combines neural networks and genetic algorithms. They are used to optimize neural networks for various applications, such as predicting academic information based on student performance. The genetic algorithm is used to optimize the neural network by generalizing the distribution of variables. This hybrid approach has been found to be effective in improving the quality of classifiers in data mining.