Methods of analysis of multimodal data to increase the accuracy of classification

Abstract

This paper proposes methods for analyzing multimodal data that will help improve the overall accuracy of the results and plans
for classifying K-Nearest Neighbor (KNN) to minimize their risk. The mechanism of increasing the accuracy of KNN classification
is considered. The research methods used in this work are comparison, analysis, induction, and experiment. This work aimed to
improve the accuracy of KNN classification by comparing existing algorithms and applying new methods. Many literary and media
sources on the classification according to the algorithm k of the nearest neighbors were analyzed, and the most exciting variations of
the given algorithm were selected. Emphasis will be placed on achieving maximum classification accuracy by comparing existing
and improving methods for choosing the number k and finding the nearest class. Algorithms with and without data analysis and preprocessing are also compared. All the strategies discussed in this article will be achieved purely practically. An experimental
classification by k nearest neighbors with different variations was performed. Data for the experiment used two different data sets of
various sizes. Different classifications k and the test sample size were taken as classification arguments. The paper studies three
variants of the algorithm k nearest neighbors: the classical KNN, KNN with the lowest average and hybrid KNN. These algorithms
are compared for different test sample sizes for other numbers k. The article analyzes the data before classification. As for selecting
the number k, no simple method would give the maximum result with great accuracy. The essence of the algorithm is to find k
closest to the sample of objects already classified by predefined and numbered classes. Then, among these k objects, you need to
count how often the class occurs and assign the most common class to the selected object. If two classes' occurrences are the largest
and the same, the class with the smaller number is assigned.