Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor

Priyadarshini, J.; Premalatha, M.; Čep, Róbert; Jayasudha, M.; Kalita, Kanak

doi:10.3390/app13020906

Cited by 31 publications

(12 citation statements)

References 33 publications

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“…The findings determined that kNN can classify with 96.83% when Ant Colony Optimization is used as the feature selection method. Another study aiming to classify breast cancer dataset with kNN and using feature selection was proposed by Priyadarshini et al in [38]. The authors applied various feature selection methods to various datasets, including a breast cancer dataset, and then classified them with kNN.…”

Section: Literature Reviewmentioning

confidence: 99%

An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis

Şenol,

Kaya

2024

Türk Doğa Ve Fen Dergisi

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Classification algorithms are commonly used as a decision support system for diagnosing many diseases, such as breast cancer. The accuracy of classification algorithms can be affected negatively if the data contains outliers and/or noisy data. For this reason, outlier detection methods are frequently used in this field. In this study, we propose and compare various models that use clustering algorithms to detect outliers in the data preprocessing stage of classification to investigate their effects on classification accuracy. Clustering algorithms such as DBSCAN, HDBSCAN, OPTICS, FuzzyCMeans, and MCMSTClustering (MCMST) were used separately in the data preprocessing stage of the k Nearest Neighbor (kNN) classification algorithm for outlier elimination, and then the results were compared. According to the obtained results, MCMST algorithm was more successful in outlier elimination. The classification accuracy of the kNN + MCMST model was 0.9834, which was the best one, while the accuracy of kNN algorithm without using any data preprocessing was 0.9719.

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Section: Literature Reviewmentioning

confidence: 99%

An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis

Şenol,

Kaya

2024

Türk Doğa Ve Fen Dergisi

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“…The performance of these algorithms were compared using factors such as accuracy, processing cost, suitability, average of selected features and convergence capabilities. The results showed that Equilibrium Optimizer (EO) had a better performance than other algorithms and it was suggested to solve problems related to feature selection 29 .…”

Section: Related Literaturementioning

confidence: 99%

Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction

Noroozi,

Orooji,

Erfannia

2023

Sci Rep

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The present study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Then seven algorithms Bayes net, Naïve Bayes (BN), multivariate linear model (MLM), Support Vector Machine (SVM), logit boost, j48, and Random Forest were applied to identify the best models for heart disease prediction. Precision, F-measure, Specificity, Accuracy, Sensitivity, ROC area, and PRC were measured to compare feature selection methods' effect on prediction algorithms. The results demonstrate that feature selection resulted in significant improvements in model performance in some methods (e.g., j48), whereas it led to a decrease in model performance in other models (e.g. MLP, RF). SVM-based filtering methods have a best-fit accuracy of 85.5. In fact, in a best-case scenario, filtering methods result in + 2.3 model accuracy. SVM-CFS/information gain/Symmetrical uncertainty methods have the highest improvement in this index. The filter feature selection methods with the highest number of features selected outperformed other methods in terms of models' ACC, Precision, and F-measures. However, wrapper-based and evolutionary algorithms improved models' performance from sensitivity and specificity points of view.

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“…In this paper, we integrate the information feedback model (IFM) [13] into the original Sine-Cosine Algorithm framework [31,32] to tackle many-objective optimization problems. The model's underlying concept involves using information from prior iterations of individuals to influence offspring generation.…”

Section: Related Workmentioning

confidence: 99%

A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

Narayanan¹,

Ganesh

Čep

et al. 2023

Mathematics

Self Cite

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In recent times, numerous innovative and specialized algorithms have emerged to tackle two and three multi-objective types of problems. However, their effectiveness on many-objective challenges remains uncertain. This paper introduces a new Many-objective Sine–Cosine Algorithm (MaOSCA), which employs a reference point mechanism and information feedback principle to achieve efficient, effective, productive, and robust performance. The MaOSCA algorithm’s capabilities are enhanced by incorporating multiple features that balance exploration and exploitation, direct the search towards promising areas, and prevent search stagnation. The MaOSCA’s performance is evaluated against popular algorithms such as the Non-dominated sorting genetic algorithm-III (NSGA-III), the Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) integrated with Differential Evolution (MOEADDE), the Many-objective Particle Swarm Optimizer (MaOPSO), and the Many-objective JAYA Algorithm (MaOJAYA) across various test suites, including DTLZ1-DTLZ7 with 5, 9, and 15 objectives and car cab design, water resources management, car side impact, marine design, and 10-bar truss engineering design problems. The performance evaluation is carried out using various performance metrics. The MaOSCA demonstrates its ability to achieve well-converged and diversified solutions for most problems. The success of the MaOSCA can be attributed to the multiple features of the SCA optimizer integrated into the algorithm.

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Analyzing Physics-Inspired Metaheuristic Algorithms in Feature Selection with K-Nearest-Neighbor

Cited by 31 publications

References 33 publications

An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis

An Investigation on the Use of Clustering Algorithms for Data Preprocessing in Breast Cancer Diagnosis

Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction

A Novel Many-Objective Sine–Cosine Algorithm (MaOSCA) for Engineering Applications

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