Binary coyote optimization algorithm for feature selection

Souza, Rodrigo Clemente Thom de; Macedo, Camila Andrade de; Coelho, Leandro dos Santos; Pierezan, Juliano; Mariani, Viviana Cocco

doi:10.1016/j.patcog.2020.107470

Cited by 77 publications

(21 citation statements)

References 45 publications

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“…Then we make another comparison with all the algorithms presented in ( Too & Mirjalili, 2021 ) from which a total of 9 algorithms is imported. The other mentioned eight well-known algorithms in there are; Binary Dragonfly Algorithm (BDA)( Mirjalili, 2016 ), binary artificial bee colony (BABC) ( He, Xie, Wong, & Wang, 2018 ), binary multiverse optimizer (BMVO) ( Al-Madi, Faris, & Mirjalili, 2019 ), binary particle swarm optimization (BPSO) ( Kennedy & Eberhart, 1997 ), chaotic crow search algorithm (CCSA) ( Sayed, Hassanien, & Azar, 2019 ), binary coyote optimization algorithm (BCOA) ( de Souza, de Macedo, dos Santos Coelho, Pierezan, & Mariani, 2020 ), evolution strategy with covariance matrix adaptation (CMAES) ( Hansen & Kern, 2004 ), and success-history based adaptive differential evolution with linear population size reduction (LSHADE) ( Tanabe & Fukunaga, 2014 ). Table 1 presents the parameters that are used by these algorithms.…”

Section: Resultsmentioning

confidence: 99%

An empowered AdaBoost algorithm implementation: A COVID-19 dataset study

Sevinç

2022

Computers & Industrial Engineering

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The Covid-19 outbreak, which emerged in 2020, became the top priority of the world. The fight against this disease, which has caused millions of people’s deaths, is still ongoing, and it is expected that these studies will continue for years. In this study, we propose an improved learning model to predict the severity of the patients by exploiting a combination of machine learning techniques. The proposed model uses an adaptive boost algorithm with a decision tree estimator and a new parameter tuning process. The learning ratio of the new model is promising after many repeated experiments are performed by using different parameters to reduce the effect of selecting random parameters. The proposed algorithm is compared with other recent state-of-the-art algorithms on UCI data sets and a recent Covid-19 dataset. It is observed that competitive accuracy results are obtained, and we hope that this study unveils more usage of advanced machine learning approaches.

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Section: Resultsmentioning

confidence: 99%

An empowered AdaBoost algorithm implementation: A COVID-19 dataset study

Sevinç

2022

Computers & Industrial Engineering

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“…The COA optimizer is a swarm intelligence algorithm that considers the social relations of the Canis latrans species and its adaptation to the environment proposed by [41] devoted to solving optimization problems. Therefore, the COA mechanism has been designed based on the social conditions of the coyotes, which means the decision variables x of a global optimization problem [54].…”

Section: Coyote Optimization Algorithmmentioning

confidence: 99%

“…This algorithm has recently been applied in several applications, especially to feature selection [54], tune heavy-duty gas turbine hyperparameters [55], optimal power flow for transmission power networks [56] define networks reconfiguration [57], and for optimal parameter estimation of a proton exchange membrane fuel cell [58]. Due to the promising potentials results, a search of the literature reveals that the COA has not yet been applied for the CEEMD's hyperparameters definition, then it is adopted.…”

Section: Coyote Optimization Algorithmmentioning

confidence: 99%

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

et al. 2020

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Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in the Brazilian market. Exogenous variables, such as supply, lagged prices and demand are considered as inputs signals of the forecasting model. Firstly, the coyote optimization algorithm is adopted to tune the hyperparameters of complementary ensemble empirical mode decomposition in the pre-processing phase. Next, three machine learning models, including extreme learning machine, gradient boosting machine, and support vector regression models, as well as Gaussian process, are designed with the intent of handling the components obtained through the signal decomposition approach with focus on time series forecasting. The individual forecasting models are directly integrated in order to obtain the final forecasting prices one to three-months-ahead. In this case, a grid of forecasting models is obtained. The best forecasting model is the one that has better generalization out-of-sample. The empirical results show the efficiency of the proposed model. Additionally, it can achieve forecasting errors lower than 4.2% in terms of symmetric mean absolute percentage error. The ranking of importance of the variables, from the smallest to the largest is, lagged prices, demand, and supply. This paper provided useful insights for multi-step-ahead forecasting in the electrical market, once the proposed model can enhance forecasting accuracy and stability.

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“…Although the wrapper-based methods are computationally expensive and their performance depends on the utilized learning algorithm, they are usually more accurate than the other two categories [13]. The wrapper-based methods use different search approaches such as exhaustive, random, greedy, heuristic, and metaheuristic [14], which, except for the last search approach, are impractical to select effective features from medium and large datasets [15]. Thus, a wide range of metaheuristic optimization algorithms is proposed to solve the feature selection problems for applications with large datasets such as medicine [16].…”

Section: Introductionmentioning

confidence: 99%

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

et al. 2021

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Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.

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Binary coyote optimization algorithm for feature selection

Cited by 77 publications

References 45 publications

An empowered AdaBoost algorithm implementation: A COVID-19 dataset study

An empowered AdaBoost algorithm implementation: A COVID-19 dataset study

Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning

B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

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