Weighted distance classification method based on data intelligence

Abstract

AbstractIn today's world, data is essential for enhancing an organization's development and decision‐making processes. Implementing artificial intelligence is necessary to analyse data and make meaningful recommendations. Machine learning distance classification methods are used to classify observations in various algorithms, such as K‐nearest neighbours (KNN), learning vector quantization and support vector machines, and are commonly used in academia and industry. However, this procedure faces a significant challenge in finding optimal parameters (i.e., distance metrics and the desired number of neighbours) in multidimensional datasets. This study presents a novel variation of a general method for classifying new observations. The method defines a new measure called closeness, which represents the proximity between an observation and the distribution. The advantages of this method are the use of both parametric and non‐parametric distance metrics and the ability to classify observations in cases where the simple method does not provide a clear answer. This method was demonstrated using KNN over three datasets and was observed to succeed in providing correct classifications, while the simple KNN method did not. The results showed that the proposed method increased the accuracy score to 40.7% in two of the three cases and that the closeness values were well defined by the proximity between the new observation and the given distribution. In addition, the F1 score increased up to 47.97%. The innovative method introduced here may be examined and used in various distance classification algorithms.