Supervised cluster classification using the originaln-dimensional space without transformation into lower dimension

The rapid development of information technology has led to the development of medical informatization in the direction of intelligence. Medical health big data provides a basic data resource guarantee for medical service intelligence and smart healthcare. The classification of medical health big data is of great significance for the intelligentization of medical information. Due to the simplicity of KNN (K-Nearest Neighbor) classification algorithm, it has been widely used in many fields. However, when the sample size is large and the feature attributes are large, the efficiency of the KNN algorithm classification will be greatly reduced. This paper proposes an improved KNN algorithm and compares it with the traditional KNN algorithm. The classification is performed in the query instance neighborhood of the conventional KNN classifier, and weights are assigned to each class. The algorithm considers the class distribution around the query instance to ensure that the assigned weight does not adversely affect the outliers. Aiming at the shortcomings of traditional KNN algorithm in processing large data sets, this paper proposes an improved KNN algorithm based on cluster denoising and density cropping. The algorithm performs denoising processing by clustering, and improves the classification efficiency of KNN algorithm by speeding up the search speed of K-nearest neighbors, while maintaining the classification accuracy of KNN algorithm. The experimental results show that the proposed algorithm can effectively improve the classification efficiency of KNN algorithm in processing large data sets, and maintain the classification accuracy of KNN algorithm well, and has good classification performance. INDEX TERMS Improved KNN classifier, weighted KNN algorithm, cluster denoising, density cropping.

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Medical Health Big Data Classification Based on KNN Classification Algorithm

Xing

Bei

2020

IEEE Access

206

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Method of cluster making for a multivariate analysis.

2003

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Cancer Prediction Based on Radical Basis Function Neural Network with Particle Swarm Optimization

Yan

Xiong²,

Hu³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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This paper addresses cancer prediction based on radial basis function neural network optimized by particle swarm optimization. Today, cancer hazard to people is increasing, and it is often difficult to cure cancer. The occurrence of cancer can be predicted by the method of the computer so that people can take timely and effective measures to prevent the occurrence of cancer. In this paper, the occurrence of cancer is predicted by the means of Radial Basis Function Neural Network Optimized by Particle Swarm Optimization. The neural network parameters to be optimized include the weight vector between network hidden layer and output layer, and the threshold of output layer neurons. The experimental data were obtained from the Wisconsin breast cancer database. A total of 12 experiments were done by setting 12 different sets of experimental result reliability. The findings show that the method can improve the accuracy, reliability and stability of cancer prediction greatly and effectively.

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Supervised cluster classification using the originaln-dimensional space without transformation into lower dimension

Cited by 3 publications

References 9 publications

Medical Health Big Data Classification Based on KNN Classification Algorithm

Medical Health Big Data Classification Based on KNN Classification Algorithm

Method of cluster making for a multivariate analysis.

Cancer Prediction Based on Radical Basis Function Neural Network with Particle Swarm Optimization

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