KNN Classification with One-step Computationdoi:10.1109/TKDE.2021.3119140Shichao ZhangJiaye LiInstitute of Electrical and Electronics Engineers (IEEE)
Based on the aggregation in Step 3, KNN predicts the class (for classification tasks) or value (for regression tasks) of the query instance. This prediction is made without the need for an explicit model, as KNN uses the dataset itself and the distances calculated to make predictions. 2.2: ...
In summary, there is no privacy-preserving KNN algorithm that can simultaneously realize the privacy of data, query, and classification results based on only one cloud server. 1.2. Our Contributions In this paper, we propose a privacy-preserving KNN classification (PPKC) algorithm with only one ...
CVKNNMdl is a ClassificationPartitionedModel classifier. Compare the classifier with one that uses a different weighting scheme. Get w2 = [0.2; 0.2; 0.3; 0.3]; CVKNNMdl2 = fitcknn(X,Y,'Distance',@(x,Z)chiSqrDist(x,Z,w2),... 'NumNeighbors',k,'KFold',10,'Standardize',1); cla...
Experimental results demonstrated the goodness of the diffusion mechanism for several computer vision tasks: image retrieval, semi-supervised and supervised learning, image classification. Diffusion requires the construction of a kNN graph in order to work. As predictable, the quality of the created grap...
Mammogram is the best one in the currently used technique for diagnosing breast cancer. In this paper, the retrieval process is divided into four distinct parts that are feature extraction, kNN classification, pattern instantiation and computation of pattern similarity. In feature extraction step, low...
One of the benefits of KNN is its simplicity, although there have been extensive studies from several viewpoints of the KNN classifier to improve the accuracy of the classification and reduce its defects. The most important disadvantage of it is the necessity to store the complete training set wh...
The purpose of this study is to improve the computational performance of one of the supervised learning methods, namely KNN, in building a clinical trial document text classification model by combining KNN and the fine-grained algorithm. This research contributed to increasing the computational ...
We can understand Neighbors-based classification with the help of following two characteristics −It is computed from a simple majority vote of the nearest neighbors of each point. It simply stores instances of the training data, that’s why it is a type of non-generalizing learning....
CVKNNMdl is a ClassificationPartitionedModel classifier. Compare the classifier with one that uses a different weighting scheme. Get w2 = [0.2; 0.2; 0.3; 0.3]; CVKNNMdl2 = fitcknn(X,Y,'Distance',@(x,Z)chiSqrDist(x,Z,w2),... 'NumNeighbors',k,'KFold',10,'Standardize',1); cla...