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RUCIB: a novel rule-based classifier based on BRADO algorithm

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Abstract

Classification is a widely used supervised learning technique that enables models to discover the relationship between a set of features and a specified label using available data. Its applications span various fields such as engineering, telecommunication, astronomy, and medicine. In this paper, we propose a novel rule-based classifier called RUCIB (RUle-based Classifier Inspired by BRADO), which draws inspiration from the socio-inspired swarm intelligence algorithm known as BRADO. RUCIB introduces two key aspects: the ability to accommodate multiple values for features within a rule and the capability to explore all data features simultaneously. To evaluate the performance of RUCIB, we conducted experiments using ten databases sourced from the UCI machine learning database repository. In terms of classification accuracy, we compared RUCIB to ten well-known classifiers. Our results demonstrate that, on average, RUCIB outperforms Naive Bayes, SVM, PART, Hoeffding Tree, C4.5, ID3, Random Forest, CORER, CN2, and RACER by 9.32%, 8.97%, 7.58%, 7.4%, 7.34%, 7.34%, 7.22%, 5.06%, 5.01%, and 1.92%, respectively.

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Data availablity

The data that support the findings of this study are openly available in Mendeley Data at https://data.mendeley.com/datasets/m7vprf5b6k.

Notes

  1. http://archive.ics.uci.edu/ml.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Control and Computer Engineering, Politecnico di Torino, 10129, Turin, Italy

    Iman Morovatian

  2. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran

    Alireza Basiri

  3. Leiden Institute of Advanced Computer Science (LIACS), Leiden University, P.O. Box 9512, 2300 RA, Leiden, The Netherlands

    Samira Rezaei

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  1. Iman Morovatian
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  3. Samira Rezaei
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Iman Morovatian, Alireza Basiri, and Samira Rezaei. The first draft of the manuscript was written by Iman Morovatian and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Alireza Basiri.

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Morovatian, I., Basiri, A. & Rezaei, S. RUCIB: a novel rule-based classifier based on BRADO algorithm. Computing 106, 495–519 (2024). https://doi.org/10.1007/s00607-023-01226-1

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