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Study of Machine Learning Classification Algorithms to Predict Accuracy and Performance of Liver Disease
Published in Rajesh Singh, Anita Gehlot, P.S. Ranjit, Dolly Sharma, Futuristic Sustainable Energy and Technology, 2022
Pawan Kumar Singh, Virendra Pal Singh, Pankaj Sharma, Durgesh Narayan
The k-Nearest Neighbor algorithm is based on an examination of an undefined example and the k training instances that are the next neighbor to this example. The first stage to implement the k-nearest Neighbor method is to locate the k-closest training sample. The n characteristics are specified as “Closeness,” given in the training Example-Set in n-dimensional space. By utilizing different metrics, for example Euclidean distance, the differences between the unknown example and the training example may be determined. Data before training and application of K-Nearest Neighbor should be normalized as distances are always dependent on absolute values. The operator parameters define the measure and the specific set up of the system. In the second phase, the unknown example is classified by a plurality vote by the found neighbors. The algorithm for the k-next neighbor. The estimated value in the case of a regression is the sum of the found neighborhood’s values. The neighbors’ contribution will be helpful to sum up, so that neighbors add more to the average than those that are further apart. Once we compete the setup and execution of k-NN classifier as shown in Figure 7. It has observed that true prediction of class1 is about 75.84% having class recall is 78.26% and class2 is about 40.94% having class recall is 37.68%.
Advanced Data Analytics
Published in Ali Soofastaei, Data Analytics Applied to the Mining Industry, 2020
K-nearest neighbor algorithm uses object classification in the nearest training class and is one of the most common algorithms in data mining and extraction classifying issues. The closest neighbors are allocated an entity. It works like that. The quality of this technology depends on the weighted qualifications. Some of the problems with this approach are as follows: Depending heavily on the K value, which is a measure of how the area is calculated.The method lacks the capacity for the distinction between near and distant neighbors.In the vicinity of neighbors, overlap or noise may occur [40].
Supervised Learning
Published in Peter Wlodarczak, Machine Learning and its Applications, 2019
As mentioned before, choosing k is crucial for the k-nearest neighbor algorithm since different values for k might yield different predictions. Since k is based on majority voting, a small k makes the prediction less stable since only a few instances decide on the class assignment. As k gets bigger, the decision boundary becomes less distinct. Finding an optimal value for k is not an easy task and requires some trial and error. We can start with a low k and determine the accuracy of the prediction. We then increase k until the accuracy does not improve anymore or gets worse. It is good practice to have an odd number for k, otherwise we might end up with a draw, e.g., for binary classification 2 votes for class A and 2 votes for class B.
A novel graph neural networks approach for 3D product model retrieval
Published in International Journal of Computer Integrated Manufacturing, 2023
Chengfeng Jian, Yiqiang Lu, Mingliang Lin, Meiyu Zhang
When the graph database is large and the computing efficiency is fast enough, hamming distance can be used as the distance measurement and the graph embedded vector can be set to the form of second-order system, namely . In this way, the efficient k-nearest neighbor algorithm can be applied. The loss function is as follows: