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

Author

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  • Francisco J. Camacho-Urriolagoitia

    (Centro de Investigación en Computación del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, Gustavo A. Madero, Mexico City 07738, Mexico)

  • Yenny Villuendas-Rey

    (Centro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, Gustavo A. Madero, Mexico City 07700, Mexico)

  • Cornelio Yáñez-Márquez

    (Centro de Investigación en Computación del Instituto Politécnico Nacional, Juan de Dios Bátiz s/n, Gustavo A. Madero, Mexico City 07738, Mexico)

  • Miltiadis Lytras

    (School of Business, Deree—The American College of Greece, 6 Gravias Street, GR-153 42 Aghia Paraskevi, 15342 Athens, Greece
    College of Engineering, Effat University, Jeddah 21478, Saudi Arabia)

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.

Suggested Citation

  • Francisco J. Camacho-Urriolagoitia & Yenny Villuendas-Rey & Cornelio Yáñez-Márquez & Miltiadis Lytras, 2023. "Novel Features and Neighborhood Complexity Measures for Multiclass Classification of Hybrid Data," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:3:p:1995-:d:1042327
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    References listed on IDEAS

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    1. Francisco J. Camacho-Urriolagoitia & Yenny Villuendas-Rey & Itzamá López-Yáñez & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez, 2022. "Correlation Assessment of the Performance of Associative Classifiers on Credit Datasets Based on Data Complexity Measures," Mathematics, MDPI, vol. 10(9), pages 1-16, April.
    2. Chloe Satinet & François Fouss, 2022. "A Supervised Machine Learning Classification Framework for Clothing Products’ Sustainability," Sustainability, MDPI, vol. 14(3), pages 1-26, January.
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