Multi-strategy assisted chaotic coot-inspired optimization algorithm for medical feature selection: A cervical cancer behavior risk study

Hu, Gang; Zhong, Jingyu; Wei, Guo

doi:10.1016/j.compbiomed.2022.106239

Cited by 27 publications

(5 citation statements)

References 88 publications

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“…Enhanced optimized performance through more diverse test sets more challenging engineering applications for detailed testing. In addition, image feature selection [75,76], multi-objective problems [77], image segmentation [78,79], path planning [80][81][82], truss topology optimization [83], and shape optimization [84] can all be experimentally solved with FLAS.…”

Section: Conclusion and Future Prospectsmentioning

confidence: 99%

Multi-Strategy Boosted Fick’s Law Algorithm for Engineering Optimization Problems and Parameter Estimation

Yan,

Hu,

Zhang

2024

Biomimetics

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To address the shortcomings of the recently proposed Fick’s Law Algorithm, which is prone to local convergence and poor convergence efficiency, we propose a multi-strategy improved Fick’s Law Algorithm (FLAS). The method combines multiple effective strategies, including differential mutation strategy, Gaussian local mutation strategy, interweaving-based comprehensive learning strategy, and seagull update strategy. First, the differential variation strategy is added in the search phase to increase the randomness and expand the search degree of space. Second, by introducing the Gaussian local variation, the search diversity is increased, and the exploration capability and convergence efficiency are further improved. Further, a comprehensive learning strategy that simultaneously updates multiple individual parameters is introduced to improve search diversity and shorten the running time. Finally, the stability of the update is improved by adding a global search mechanism to balance the distribution of molecules on both sides during seagull updates. To test the competitiveness of the algorithms, the exploration and exploitation capability of the proposed FLAS is validated on 23 benchmark functions, and CEC2020 tests. FLAS is compared with other algorithms in seven engineering optimizations such as a reducer, three-bar truss, gear transmission system, piston rod optimization, gas transmission compressor, pressure vessel, and stepped cone pulley. The experimental results verify that FLAS can effectively optimize conventional engineering optimization problems. Finally, the engineering applicability of the FLAS algorithm is further highlighted by analyzing the results of parameter estimation for the solar PV model.

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Section: Conclusion and Future Prospectsmentioning

confidence: 99%

Multi-Strategy Boosted Fick’s Law Algorithm for Engineering Optimization Problems and Parameter Estimation

Yan,

Hu,

Zhang

2024

Biomimetics

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“…Feature selection is a common method for recognizing emotions and reducing dimensionality [ 7 , 8 , 9 ]. Metaheuristic algorithms have universal and diverse heuristic strategies [ 10 , 11 ], and they are powerful tools for handling complex optimization problems such as feature selection [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Yue,

Hu,

Zhu

2024

Biomimetics

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Speech emotion recognition based on gender holds great importance for achieving more accurate, personalized, and empathetic interactions in technology, healthcare, psychology, and social sciences. In this paper, we present a novel gender–emotion model. First, gender and emotion features were extracted from voice signals to lay the foundation for our recognition model. Second, a genetic algorithm (GA) processed high-dimensional features, and the Fisher score was used for evaluation. Third, features were ranked by their importance, and the GA was improved through novel crossover and mutation methods based on feature importance, to improve the recognition accuracy. Finally, the proposed algorithm was compared with state-of-the-art algorithms on four common English datasets using support vector machines (SVM), and it demonstrated superior performance in accuracy, precision, recall, F1-score, the number of selected features, and running time. The proposed algorithm faced challenges in distinguishing between neutral, sad, and fearful emotions, due to subtle vocal differences, overlapping pitch and tone variability, and similar prosodic features. Notably, the primary features for gender-based differentiation mainly involved mel frequency cepstral coefficients (MFCC) and log MFCC.

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“…In the process of feature extraction, there are many aspects of feature extraction, such as amino acid composition, protein secondary structure information, and physical and chemical properties of protein sequences, which play an important role in the identification of antioxidant proteins 5–7 . In the process of feature selection, we should not only select feature combinations with high contribution but also consider that the dimension of features should not be too high 8–10 . A dimension that is too high will affect not only the efficiency of the model but also the accuracy of the model due to redundant features.…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7] In the process of feature selection, we should not only select feature combinations with high contribution but also consider that the dimension of features should not be too high. [8][9][10] A dimension that is too high will affect not only the efficiency of the model but also the accuracy of the model due to redundant features. This paper discusses several commonly used feature selection methods, such as analysis of variance (ANOVA), 11 max advantage and min redundancy (MRMR), 12 and max advantage and min redundancy (MRMD), [13][14][15] and several machine learning algorithms, such as random forest (RF), 16,17 support vector machine (SVM), [18][19][20] and Knearest neighbor (KNN), 21 and compares their principles and effects on model performance.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based antioxidant protein identification model: Progress and evaluation

Meng,

Pei,

et al. 2023

J of Cellular Biochemistry

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Efficient and accurate identification of antioxidant proteins is of great significance. In recent years, many models for identifying antioxidant proteins have been proposed, but the low sensitivity and high dimensionality of the models are common problems. The generalization ability of the model needs to be improved. Researchers have tried different feature extraction algorithms and feature selection algorithms to obtain the most effective feature combination and have chosen more appropriate classification algorithms and tools to improve model performance. In this article, we systematically reviewed the data set of the most frequently used antioxidant proteins and the method selection for each step of model establishment and discussed the characteristics of each method. We have conducted a detailed analysis of recent research and believe that the practical ability and efficiency of model application can be improved by reducing model dimensions. The key to improving the performance of antioxidant protein recognition models in the future may lie in feature selection, so this paper also focuses on the combination of feature extraction and selection steps in the analysis of the model building process.

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Multi-strategy assisted chaotic coot-inspired optimization algorithm for medical feature selection: A cervical cancer behavior risk study

Cited by 27 publications

References 88 publications

Multi-Strategy Boosted Fick’s Law Algorithm for Engineering Optimization Problems and Parameter Estimation

Multi-Strategy Boosted Fick’s Law Algorithm for Engineering Optimization Problems and Parameter Estimation

Gender-Driven English Speech Emotion Recognition with Genetic Algorithm

Machine learning‐based antioxidant protein identification model: Progress and evaluation

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