k-NN improvement to data analysis

  • Authors

    • Tonya P. Mateva College of Dobrich, Shumen University, Dobrich, Bulgaria
    • Ivan G. Ivanov Sofia University St. Kl. Ohridski
    2019-11-05
    https://doi.org/10.14419/ijet.v8i4.29803
  • Classification Problems, Data Analysis, K Nearest Neighbors (K-NN), Machine Learning.

  • Abstract

    The problem to classify big data is an actual one the subject. There are multiple ways to classify data but the k Nearest Neighbors (k-NN) has become a popular tool for the data scientist. In this paper we examine several modifications of the k Nearest Neighbors algorithm that achieve better efficiency in terms of accuracy and CPU time when classifying test observations in comparison to the standard k Nearest Neighbors algorithm. To make the modifications faster than standard k-NN we use a special methodology which splits the input dataset into n folds and combine it with input data transformations. Each time we execute the process, one of the folds is saved as a test subset and the rest of the folds are applied for training. The process is executed n times. In the proposed methodology we are looking for the pair of subsets which produces the highest accuracy result.

     

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  • How to Cite

    P. Mateva, T., & G. Ivanov, I. (2019). k-NN improvement to data analysis. International Journal of Engineering & Technology, 8(4), 523-526. https://doi.org/10.14419/ijet.v8i4.29803

    Received date: 2019-08-30

    Accepted date: 2019-09-28

    Published date: 2019-11-05