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Computing
Published in: Computing 2/2024

31-10-2023 | Regular Paper

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

Authors: Iman Morovatian, Alireza Basiri, Samira Rezaei

Published in: Computing | Issue 2/2024

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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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Footnotes
Literature
1.
Rezaei S, McKean JP, Biehl M, de Roo W, Lafontaine A (2022) A machine learning based approach to gravitational lens identification with the International LOFAR Telescope. Mon Not R Astron Soc 517(1):1156–1170CrossRef
2.
Rezaei S, McKean J, Biehl M, Javadpour A (2022) DECORAS: detection and characterization of radio-astronomical sources using deep learning. Mon Not R Astron Soc 510(4):5891–5907CrossRef
3.
Pota M, Esposito M, De Pietro G (2017) Designing rule-based fuzzy systems for classification in medicine. Knowl-Based Syst 124:105–132CrossRef
4.
5.
Rezaei S, Radmanesh H, Alavizadeh P, Nikoofar H, Lahouti F (2016) Automatic fault detection and diagnosis in cellular networks using operations support systems data. In: NOMS 2016-2016 IEEE/IFIP network operations and management symposium, pp 468–473
6.
Aggarwal CC et al (2015) Data mining: the textbook, vol 1. Springer, BerlinCrossRef
7.
Chakraborty M, Biswas SK, Purkayastha B (2020) Data mining using neural networks in the form of classification rules: a review. In: 2020 4th international conference on computational intelligence and networks (CINE), pp 1–6
8.
Berrar D (2018) Bayes’ theorem and naive Bayes classifier. Encycl Bioinform Comput Biol ABC Bioinform 403:412
9.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297CrossRef
10.
Mastrogiannis N, Boutsinas B, Giannikos I (2009) A method for improving the accuracy of data mining classification algorithms. Comput Oper Res 36(10):2829–2839CrossRef
11.
Siami M, Gholamian MR, Basiri J, Fathian M (2011) An application of locally linear model tree algorithm for predictive accuracy of credit scoring, pp 133–142
12.
13.
Aparício D, Barata R, Bravo J, Ascensão JT, Bizarro P (2020) ARMS: automated rules management system for fraud detection. arXiv preprint arXiv:​2002.​06075
14.
15.
Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation. IEEE, pp. 4661–4667
16.
17.
Zorarpacı E, Özel SA (2021) Privacy preserving rule-based classifier using modified artificial bee colony algorithm. Expert Syst Appl 183:115437CrossRef
18.
Basiri J, Taghiyareh F, Ghorbani A (2017) Collaborative team formation using brain drain optimization: a practical and effective solution. World Wide Web 20(6):1385–1407CrossRef
19.
Rad S, Basiri A (2022) Brain drain optimization: a novel approach for task scheduling in the cloud computing. In: 2022 27th international computer conference, computer society of Iran (CSICC), pp. 1–6
20.
Frank E, Witten IH (1998) Generating accurate rule sets without global optimization
21.
22.
Basiri J, Taghiyareh F, Gazani S (2010) Corer: a new rule generator classifier. In: 2010 13th IEEE International Conference on Computational Science and Engineering. IEEE, pp 64–71
23.
Basiri J, Taghiyareh F, Faili H (2019) RACER: accurate and efficient classification based on rule aggregation approach. Neural Comput Appl 31(3):895–908CrossRef
24.
25.
26.
Li W, Han J, Pei J (2001) CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceedings 2001 IEEE international conference on data mining. IEEE, pp. 369–376
27.
Wang K, Zhou S, He Y (2000) Growing decision trees on support-less association rules. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 265–269
28.
Quinlan JR (2014) C4. 5: programs for machine learning. Elsevier, Amsterdam
29.
30.
Telikani A, Gandomi AH, Shahbahrami A (2020) A survey of evolutionary computation for association rule mining. Inf Sci 524:318–352MathSciNetCrossRef
31.
32.
Jiao L, Pan Q, Denoeux T, Liang Y, Feng X (2015) Belief rule-based classification system: extension of FRBCS in belief functions framework. Inf Sci 309:26–49CrossRef
33.
Geng X, Liang Y, Jiao L (2021) EARC: evidential association rule-based classification. Inf Sci 547:202–222CrossRef
34.
Gu X, Angelov PP (2018) Semi-supervised deep rule-based approach for image classification. Appl Soft Comput 68:53–68CrossRef
35.
Porebski S (2022) Evaluation of fuzzy membership functions for linguistic rule-based classifier focused on explainability, interpretability and reliability. Expert Syst Appl 199:117116CrossRef
36.
Singh A, Gupta G et al (2019) ANT_FDCSM: a novel fuzzy rule miner derived from ant colony meta-heuristic for diagnosis of diabetic patients. J Intell Fuzzy Syst 36(1):747–760CrossRef
37.
38.
Talebi M, Abadi M (2014) BeeMiner: a novel artificial bee colony algorithm for classification rule discovery. In: In 2014 Iranian conference on intelligent systems (ICIS). IEEE, pp. 1–5
39.
Javadpour A, Rezaei S, Li KC, Wang G, Thampi SM et al (2020) A scalable feature selection and opinion miner using whale optimization algorithm. In: Thampi SM et al (eds) Advances in signal processing and intelligent recognition systems. Springer, Singapore, pp 237–247CrossRef
40.
Zhang X, Liu C, Xue L, Zeng H (2022) Simultaneous feature selection and SVM parameter by using artificial bee colony algorithm. In: Proceedings of the 2022 6th international conference on electronic information technology and computer engineering, pp. 1737–1745
41.
42.
Javadpour A, Rezaei S, Sangaiah AK, Slowik A, Mahmoodi Khaniabadi S (2023) Enhancement in quality of routing service using metaheuristic PSO algorithm in VANET networks. Soft Comput 27:2739–2750
43.
Kuo R-J, Huang S, Zulvia FE, Liao TW (2018) Artificial bee colony-based support vector machines with feature selection and parameter optimization for rule extraction. Knowl Inf Syst 55(1):253–274CrossRef
44.
45.
Boyd SP, Vandenberghe L (2004) Convex optimization. Cambridge University Press, CambridgeCrossRef
46.
Ramírez-Gallego S et al (2016) Data discretization: taxonomy and big data challenge. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 6(1):5–21CrossRef
47.
Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann Publishers Inc., San Francisco
48.
Hosseini P, Basiri A (2022) GRACER: improving the accuracy of RACER classifier using a greedy approach. In: In 2022 27th international computer conference, computer society of Iran (CSICC). IEEE, pp. 1–6
49.
De Jong KA, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 13(2–3):161–188CrossRef
50.
Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier, Amsterdam
51.
52.
53.
Domingos P, Hulten G (2000) Mining high-speed data streams. In: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 71–80
54.
Hossin M, Sulaiman MN (2015) A review on evaluation metrics for data classification evaluations. Int J Data Min Knowl Manag Process 5(2):1CrossRef
55.
Kianmehr K, Alhajj R (2008) CARSVM: a class association rule-based classification framework and its application to gene expression data. Artif Intell Med 44(1):7–25CrossRef
Metadata
Title
RUCIB: a novel rule-based classifier based on BRADO algorithm
Authors
Iman Morovatian
Alireza Basiri
Samira Rezaei
Publication date
31-10-2023
Publisher
Springer Vienna
Published in
Computing / Issue 2/2024
Print ISSN: 0010-485X
Electronic ISSN: 1436-5057
DOI
https://doi.org/10.1007/s00607-023-01226-1

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