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    Novel Features and Neighborhood Complexity Measures for Multiclass Classification of Hybrid Data

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    Author
    Camacho-Urriolagoitia, Francisco
    Villuendas-Rey, Yenny
    Yáñez-Márquez, Cornelio
    Lytras, Miltiadis cc
    Subject
    Data Complexity Measures
    Hybrid Data
    Multiclass Data
    Supervised Classification
    Research Subject Categories::INTERDISCIPLINARY RESEARCH AREAS
    Date
    2023-01-20
    
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    Abstract
    The present capabilities for collecting and storing all kinds of data exceed the collective ability to analyze, summarize, and extract knowledge from this data. Knowledge management aims to automatically organize a systematic process of learning. Most meta-learning strategies are based on determining data characteristics, usually by computing data complexity measures. Such measures describe data characteristics related to size, shape, density, and other factors. However, most of the data complexity measures in the literature assume the classification problem is binary (just two decision classes), and that the data is numeric and has no missing values. The main contribution of this paper is that we extend four data complexity measures to overcome these drawbacks for characterizing multiclass, hybrid, and incomplete supervised data. We change the formulation of Feature-based measures by maintaining the essence of the original measures, and we use a maximum similarity graph-based approach for designing Neighborhood measures. We also use ordering weighting average operators to avoid biases in the proposed measures. We included the proposed measures in the EPIC software for computational availability, and we computed the measures for publicly available multiclass hybrid and incomplete datasets. In addition, the performance of the proposed measures was analyzed, and we can confirm that they solve some of the biases of previous ones and are capable of natively handling mixed, incomplete, and multiclass data without any preprocessing needed.
    Department
    Computer Science
    Publisher
    MDPI
    Journal title
    Sustainability
    DOI
    https://doi.org/10.3390/su15031995
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.3390/su15031995
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