An efficient feature selection using artificial fish swarm optimization and svm classifier

Nalluri, Madhusudana Rao; SaiSujana, T; Reddy, K Harshini; Swaminathan, V.

doi:10.1109/netact.2017.8076805

Cited by 16 publications

(4 citation statements)

References 12 publications

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“…The experiment results showed that the proposed method achieves good performance in finding the best subset of features. Nalluri et al developed an AFSA algorithm with SVM that finds the most valuable subset of features in a dataset [123]. They evaluated their approach on datasets having binary-and multi-labelled classes.…”

Section: Artificial Fish Swarm Algorithm (Afsa)mentioning

confidence: 99%

A comprehensive survey on recent metaheuristics for feature selection

2022

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Section: Artificial Fish Swarm Algorithm (Afsa)mentioning

confidence: 99%

A comprehensive survey on recent metaheuristics for feature selection

2022

View full text Add to dashboard Cite

“…Different types of machine learning represent different classification algorithms, and each has its own advantages. The KNN algorithm has high classification accuracy and is insensitive to abnormal samples [41,42]; SVM has the advantage of being able to handle small data samples, as well as having global optimality and a strong generalization ability [43]; RF uses the average of many decision trees in the classification process to minimize overfitting and produce good regression results, thus improving the classification accuracy [44]; GP is widely used in time-series prediction tasks because of its ability to effectively exploit correlations between features [45]; XGBoost can adjust hyperparameters to maximize model performance and prevent model overfitting, while using a gradient descent optimization algorithm to integrate decision trees sequentially to minimize model error [46,47].…”

Section: Machine Learning Modelsmentioning

confidence: 99%

Forecasting Albacore (Thunnus alalunga) Fishing Grounds in the South Pacific Based on Machine Learning Algorithms and Ensemble Learning Model

Zhang

Fan

et al. 2023

Applied Sciences

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To achieve high-precision forecasting of different grades of albacore fishing grounds in the South Pacific Ocean, we used albacore fishing data and marine environmental factors data from 2009 to 2019 as data sources. An ensemble learning model (ELM) for albacore fishing grounds forecasting was constructed based on six machine learning algorithms. The overall accuracy (ACC), fishing ground forecast precision (P) and recall (R) were used as model accuracy evaluation metrics, to compare and analyze the accuracy of different machine learning algorithms for fishing grounds forecasting. We also explored the forecasting capability of the ELM for different grades of fishing grounds. A quantitative evaluation of the effects of different marine environmental factors on the forecast accuracy of albacore tuna fisheries was conducted. The results of this study showed the following: (1) The ELM achieved high accuracy forecasts of albacore fishing grounds (ACC = 86.92%), with an overall improvement of 4.39~19.48% over the machine learning models. (2) A better forecast accuracy (R2 of 81.82–98%) for high-yield albacore fishing grounds and a poorer forecast accuracy (R1 of 47.37–96.15%) for low-yield fishing grounds were obtained for different months based on the ELM; the high-yield fishing grounds were distributed in the sea south of 10° S. (3) A feature importance analysis based on RF found that latitude (Lat) had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades from February to December (0.377), and Chl-a had the greatest influence on the forecast accuracy of albacore tuna fishing grounds of different grades in January (0.295), while longitude (Lon) had the smallest effect on the forecast of different grades of fishing grounds (0.037).

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“…Several swarm intelligence algorithms have been used in the attributes selection [9][10][11][12][13][14][15][16][17]. Unfortunately, no single stable strategy exists to reduce the burden of computing and extracting highly correlated risk factors to the data and improve the classifier performance and achieve high accuracy.…”

Section: Bat Algorithm For Feature Selectionmentioning

confidence: 99%

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Sagban

Marhoon

Alubady

2020

IJECE

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Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.

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An efficient feature selection using artificial fish swarm optimization and svm classifier

Cited by 16 publications

References 12 publications

A comprehensive survey on recent metaheuristics for feature selection

A comprehensive survey on recent metaheuristics for feature selection

Forecasting Albacore (Thunnus alalunga) Fishing Grounds in the South Pacific Based on Machine Learning Algorithms and Ensemble Learning Model

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

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