Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction

Zhu, Yean; Xu, Weiyi; Luo, Guoliang; Wang, Haolun; Yang, Jingjing; Lü, Wei

doi:10.1016/j.artmed.2020.101811

Cited by 47 publications

(38 citation statements)

References 48 publications

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“…Clustering and predicting vital signs by RF is the goal of [ 43 ]. Zhu et al [ 44 ] optimize the parameters of the random forest by improved fish Swarm algorithm for predicting the knee contact force. A method for identifying foreign particles for quality detection of liquid pharmaceutical products is presented by [ 45 ].…”

Section: Related Workmentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

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Section: Related Workmentioning

confidence: 99%

A New Random Forest Algorithm Based on Learning Automata

Savargiv

Masoumi

Keyvanpour

2021

Computational Intelligence and Neuroscience

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“…During the process of test, IAFSA is compared with particle swarm optimization algorithm (PSO) and AFSA. All the algorithms run on Python 3.6.10, and the parameter settings are the same as those in the literature (Zhu et al 2020), listed in Table 2.…”

Section: Performance Testmentioning

confidence: 99%

Forecasting annual natural gas consumption via the application of a novel hybrid model

Gao

Shao

2021

Environ Sci Pollut Res

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Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025.

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“…To test the effectiveness of AGOHS, this paper uses the 13 classic test functions mentioned in the reference [33][34][35] as the benchmark function. Compared with three improved HS algorithms, such as IDHS [30], HSDM [31], and ID-HS-LDD [32], the optimization results are compared and analyzed under the condition that each of the 13 test function optimization operations is run 50 times separately.…”

Section: Experiments and Related Analysismentioning

confidence: 99%

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

Zhou

et al. 2020

IEEE Access

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Compared with rules in the form of 'IF-THEN,' weighted fuzzy production rules (WFPRs) have more robust knowledge expression capabilities, but weighted fuzzy production rules are more difficult to obtain. The weighted fuzzy production rules obtained using traditional neural network methods have shortcomings, such as insufficient precision and insufficient knowledge extraction. Focusing on the mentioned shortages, a modified weighted fuzzy production rules extraction approach is proposed by combining the modified harmony search algorithm, and neural network. The method consists of three main stages. First, a global optimal adaptive harmony search algorithm (AGOHS) is proposed to overcome the traditional harmony search algorithm's existing poor adaptive ability. Then, the AGOHS algorithm is used to optimize the neural network's initial weights to improve the neural network's training efficiency. Finally, extract the WFPRs with IF-THEN from the trained neural network and give the corresponding fuzzy reasoning. Through the WFPRs extraction experiments using IRIS and PIMA data sets reveal the proposed rule extraction framework has some apparent highlights, such as high accuracy, the smaller number of generated rules, and low redundancy.

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Random Forest enhancement using improved Artificial Fish Swarm for the medial knee contact force prediction

Cited by 47 publications

References 48 publications

A New Random Forest Algorithm Based on Learning Automata

A New Random Forest Algorithm Based on Learning Automata

Forecasting annual natural gas consumption via the application of a novel hybrid model

Weighted Fuzzy Production Rule Extraction Using Modified Harmony Search Algorithm and BP Neural Network Framework

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