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What does FRNN stand for?

FRNN stands for Fuzzy Rough Nearest Neighbor

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We then propose a hybrid fuzzy rough with K-nearest neighbor (K-NN)-based classifier (FRNN) to classify the patterns in the reduced datasets, obtained from the fuzzy rough bio-inspired algorithm search. While exploring other possible hybrid evolutionary processes, we then conducted experiments considering (i) same feature selection algorithm with support vector machine (SVM) and random forest (RF) classifier; (ii) instance based selection using synthetic minority over-sampling technique with fuzzy rough K-nearest neighbor (K-NN), SVM and RF classifier.
First, we make every training sample fuzzy-roughness and use edit nearest neighbor algorithm to remove training sample points in class boundary or overlapping regions, and then use Mountain Clustering method to select representative cluster center points, then Fuzzy-Rough Nearest neighbor algorithm (FRNN) is applied to classify the test data. The new algorithm is applied to hand gesture image recognition, the results show that it is more effective and performs better than other nearest neighbor methods.
Learning from imbalanced data sets presents a new challenge to machine learning community, as traditional methods are biased to majority classes and produce poor detection rate of minority classes. This paper presents a new approach, namely fuzzy-rough k-nearest neighbor algorithm for imbalanced data sets learning to improve the classification performance of minority class. The approach defines fuzzy membership function that is in favor of minority class and constructs fuzzy equivalent relation between the unlabeled instance and its k nearest neighbors.
Conference Paper: Fuzzy-rough nearest neighbors.