Entropy and gravitation based dynamic radius nearest neighbor classification for imbalanced problem Abstract   In imbalanced problems, the asymmetric number of samples in different classes brings great challenges to traditional classifiers, especially to the Nearest Neighbors (NN) classifiers. When NNbased classifier deals with imbalanced problems, the criterion of itself makes the classification result datadependent, thus biasing towards the majority class. To overcome the drawback in NNbased classifiers, a meta heuristic NNbased algorithm named Gravitational Fixed Radius Nearest Neighbor classifier (GFRNN) is proposed to solve imbalanced problems by drawing on Newtonxe2x80x99s law of universal gravitation. However, GFRNN still has three major problems including negligence of the distribution of samples, unreasonable calculation of data mass and improper distance metric. To this end, this paper proposes an Entropy and Gravitation based Dynamic Radius Nearest Neighbor algorithm (EGDRNN). Different from GFRNN, EGDRNN determines the radius in a dynamic and rapid way. EGDRNN uses entropy information to make samples at different locations have different importance. Finally, by utilizing a general      L    p     norm to calculate the distance between two samples, the classification performance is greatly improved. The experimental result validates that the proposed EGDRNN not only achieves the highest classification accuracy but also takes the lowest time consuming among all comparison algorithms.

Fuzzy Nearest Neighbor Partitioning Neural Network for Classification The fuzzy nearest neighbor partitioning neural network (FNNP) is proposed to promote the capability of the neural network classifier. The fixed centroids problem restrains the further improvement of the neural network classifier. The original nearest neighbor partitioning method (NNP) have been proposed to address this problem. In the NNP, the learning method is employed to train the neural network in according with the distribution information of samples. However, the distribution information underutilization problem affects the learning method to obtains the neural network with expected mapping performance. Therefore, we propose the FNNP to overcome this problem. In the FNNP, the fuzzy logic theory is adopted to assist the learning method to comprehensively collect neglected distribution information that increases the probability to find the optimal neural network with expected mapping performance. Experiment results demonstrate that the FNNP achieves remarkable classification performance on various indictors.