Прегледај по Аутор "Ognjenovic, Visnja"

Сада се приказује 1 - 2 од 2
  • Сличица
    Ставка
    Method PPC for Precise Piecewise Correlation after Histogram Segmentation
    (MDPI, 2024) Ognjenovic, Visnja; Stojanov, Jelena; Brtka, Vladimir; Blazic, Marko; Brtka, Eleonora; Berkovic, Ivana
    Correlation, functioning as a symmetric relation, is very powerful indicator of the mutual association between two attributes. The problem of weak correlation indicates a lack of linearity in the observed range. This paper presents the precise piecewise correlation method, which overcomes the problem by determining the segments where the linear association will be present. The determination was achieved using the histogram segmentation method. The conditions of the application and analysis of the method are presented, as well as the application of the method to the representative datasets. The obtained results confirm the existence of stronger linear associations on the segments. Detected correlations reveal the strength and nature of the symmetric association between two attributes on each of the separated segments.
  • Сличица
    Ставка
    The Cuts Selection Method Based on Histogram Segmentation and Impact on Discretization Algorithms
    (MDPI, 2022) Ognjenovic, Visnja; Brtka, Vladimir; Stojanov, Jelena; Brtka, Eleonora; Berkovic, Ivana
    The preprocessing of data is an important task in rough set theory as well as in Entropy. The discretization of data as part of the preprocessing of data is a very influential process. Is there a connection between the segmentation of the data histogram and data discretization? The authors propose a novel data segmentation technique based on a histogram with regard to the quality of a data discretization. The significance of a cut’s position has been researched on several groups of histograms. A data set reduct was observed with respect to the histogram type. Connections between the data histograms and cuts, reduct and the classification rules have been researched. The result is that the reduct attributes have a more irregular histogram than attributes out of the reduct. The following discretization algorithms were used: the entropy algorithm and the Maximal Discernibility algorithm developed in rough set theory. This article presents the Cuts Selection Method based on histogram segmentation, reduct of data and MD algorithm of discretization. An application on the selected database shows that the benefits of a selection of cuts relies on histogram segmentation. The results of the classification were compared with the results of the Naïve Bayes algorithm