A Survey on Fuzzy Systems Optimization Using Evolutionary Algorithms and Swarm Intelligence

Kant, Shashi; Agarwal, Devendra; Shukla, Praveen Kumar

doi:10.1007/978-981-16-8225-4_33

Cited by 8 publications

(5 citation statements)

References 115 publications

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“…In future work, the goal is to improve the result by trying other multiobjective techniques, using, for example, Pareto frontier [18]. Another approach is to use another evolutionary algorithm, such as Particle-Swarm Optimization, Ant Colony Optimization, Bee Colony Algorithm, and Bacterial Evolutionary Algorithm [14,23] to search for a SIF with better interpretability in problems of higher complexity and a large number of input variables.…”

Section: Discussion and Resultsmentioning

confidence: 99%

Optimizing explainability of Breast Cancer Recurrence using FuzzyGenetic

Cardoso,

Medeiros,

Vellasco

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Breast cancer is the most common cancer diagnosed in the world, being the cause of death of 685,000 people worldwide in 2020. Due to the aggressiveness of the disease, early-stage identification, treatment, and remission detection are important to ensure longevity to those who may have cancer. In this paper, we propose a fuzzy-genetic approach for breast cancer recurrence classification. To this end, we use a Genetic Algorithm to design automatically the fuzzy inference system with the objective of balancing between accuracy and explainability. The proposed system achieved an accuracy of 91.30%, finding eleven rules with a maximum of three antecedents per rule, which provided a competitive result compared to other Machine Learning approaches.

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Section: Discussion and Resultsmentioning

confidence: 99%

Optimizing explainability of Breast Cancer Recurrence using FuzzyGenetic

Cardoso,

Medeiros,

Vellasco

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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“…From [54] GA-RF 0.921 ± 0.000 NA 0.016 ± 0.0000 NA From [55] IGRF-RFE 0.842 ± 0.000 0.0842 NA 0.829 ± 0.000 From [10] PIO 0.913 ± 0.0003 0.897 ± 0.0002 0.052 ± 0.0004 0.904 ± 0.0002 From [56] Rule-Based 0.652 ± 0.000 0.903 ± 0.000 0.02 ± 0.000 0.681 ± 0.000 From [57] Wrapper-Based-DT 0.864 ± 0.000 0.97 ± 0.000 0.028 ± 0.000 NA The proposed model with HC PHFS-IWDHC 4 [4,10,11,6] From [54] GA-RF 9 [27,3,41,35,36,10,31,2,18] From [55] IGRF-RFE 23 [0, 1,2,3,4,5,6,7,8,9,10,12,14,15,16,18,23,24,25,26,27,31,35] From [10] PIO 14 [3,8,9,11,12,…”

Section: Unsw-nb15 Resultsmentioning

confidence: 99%

“…Overall, the proposed methods provide competitive results depending on accuracy, FPR, F-score, and TPR, compared to some of the most recent methods from the literature using the least number of features, as shown in Table 5. From [10] PIO 18 [1,3,4,5,6,8,10,11,12,13,14,15,17,18,27,32,36,39,41] From [60] PSO 37 [2,3,4,5,6,7,8,9,10,11,12,13,14,15,17,18,20,21,22,23,24,25,26,27,28,29,31,32,…”

Section: Unsw-nb15 Resultsmentioning

confidence: 99%

“…FS methods mark an important stage before building the machine-learning model. Evolutionary algorithms and swarm intelligence algorithms, such as Genetic Algorithms and ant colonies, have been successfully employed to solve the problem of feature selection [14]. These methods are based on understanding the biological behaviour of animals/insects and using this understanding to solve optimization problems.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

Alhenawi

Alazzam

Al-Sayyed

et al. 2022

Cybernetics and Information Technologies

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A critical task and a competitive research area is to secure networks against attacks. One of the most popular security solutions is Intrusion Detection Systems (IDS). Machine learning has been recently used by researchers to develop high performance IDS. One of the main challenges in developing intelligent IDS is Feature Selection (FS). In this manuscript, a hybrid FS for the IDS network is proposed based on an ensemble filter, and an improved Intelligent Water Drop (IWD) wrapper. The Improved version from IWD algorithm uses local search algorithm as an extra operator to increase the exploiting capability of the basic IWD algorithm. Experimental results on three benchmark datasets “UNSW-NB15”, “NLS-KDD”, and “KDDCUPP99” demonstrate the effectiveness of the proposed model for IDS versus some of the most recent IDS algorithms existing in the literature depending on “F-score”, “accuracy”, “FPR”, “TPR” and “the number of selected features” metrics.

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“…Previous research has demonstrated the effectiveness of these optimization algorithms in enhancing the accuracy and interpretability of fuzzy rule-based classifiers (X. Zhang et al, 2014) (Carse et al, 1996)(E. Zhou & Khotanzad, 2007)(Del Jesus et al, 2004 (Kant et al, 2022a) (Kant et al, 2022b). Optimizing Fuzzy Rule-Based Classifiers Using Genetic Algorithms by Martinez et al (2010) The interpretability of fuzzy rule-based classifiers has been a significant focus in previous research.…”

Section: Introductionmentioning

confidence: 99%

An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory

Ferguson

2023

emod

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This research presents an integrated approach for fuzzy rule generation in dataset classification by combining hybrid grid partitioning and rough set theory. The objective is to enhance the accuracy and interpretability of classification models. The approach leverages hybrid grid partitioning to achieve localized rule generation, capturing the local characteristics and patterns within different regions of the feature space. Furthermore, rough set theory is applied for attribute reduction, identifying the most relevant features and reducing the complexity of the classification problem. The generated fuzzy rules provide interpretable and understandable classification rules that facilitate domain expert interpretation. The research contributes to the field by proposing a comprehensive framework that improves both accuracy and interpretability of dataset classification. The findings demonstrate the effectiveness of the integrated approach, although certain limitations exist. Future research should focus on parameter selection, scalability challenges, and the applicability of the approach to diverse problem domains. The integrated approach presents a promising methodology for enhancing the accuracy and interpretability of dataset classification, with potential applications in various domains where accurate and interpretable classification models are crucial.

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A Survey on Fuzzy Systems Optimization Using Evolutionary Algorithms and Swarm Intelligence

Cited by 8 publications

References 115 publications

Optimizing explainability of Breast Cancer Recurrence using FuzzyGenetic

Optimizing explainability of Breast Cancer Recurrence using FuzzyGenetic

Hybrid Feature Selection Method for Intrusion Detection Systems Based on an Improved Intelligent Water Drop Algorithm

An integrated approach for fuzzy rule generation in dataset classification using hybrid grid partitioning and rough set theory

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