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A Study on GBW-KNN Using Statistical Testing
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In the 4th industrial revolution, big data and artificial intelligence are becoming more and more important. This is because the value can be four by applying artificial intelligence techniques to data generated and accumulated in real-time. Various industries utilize them to provide a variety of services and products to customers and enhance their competitiveness. The KNN algorithm is one of such analysis methods, which predicts the class of an unlabeled instance by using the classes of nearby neighbors. It is used a lot because it is simpler and easier to understand than other methods. In this study, we proposed a GBW-KNN algorithm that finds KNN after assigning weights to each individual based on the KNN graph. In addition, a statistical test was conducted to see if there was a significant difference in the performance difference between the KNN and GBW-KNN methods. As a result of the experiment, it was confirmed that the performance of GBW-KNN was excellent overall, and the difference in performance between the two methods was significant.
Title: A Study on GBW-KNN Using Statistical Testing
Description:
In the 4th industrial revolution, big data and artificial intelligence are becoming more and more important.
This is because the value can be four by applying artificial intelligence techniques to data generated and accumulated in real-time.
Various industries utilize them to provide a variety of services and products to customers and enhance their competitiveness.
The KNN algorithm is one of such analysis methods, which predicts the class of an unlabeled instance by using the classes of nearby neighbors.
It is used a lot because it is simpler and easier to understand than other methods.
In this study, we proposed a GBW-KNN algorithm that finds KNN after assigning weights to each individual based on the KNN graph.
In addition, a statistical test was conducted to see if there was a significant difference in the performance difference between the KNN and GBW-KNN methods.
As a result of the experiment, it was confirmed that the performance of GBW-KNN was excellent overall, and the difference in performance between the two methods was significant.
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