A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence

Aladeemy, Mohammed; Tutun, Salih; Khasawneh, Mohammad T.

doi:10.1016/j.eswa.2017.06.030

Cited by 59 publications

(21 citation statements)

References 46 publications

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“…This indicates that the parameter values are problem dependent. Many researchers tried to use some adaptive mechanisms to control the parameters in different optimizers [10,1,69].…”

Section: Related Workmentioning

confidence: 99%

Asynchronous accelerating multi-leader salp chains for feature selection

Aljarah

Mafarja

Heidari

et al. 2018

Applied Soft Computing

178

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Feature selection is an imperative preprocessing step that can positively affect the performance of data mining techniques. Searching for the optimal feature subset amongst an unabridged dataset is a challenging problem, especially for large-scale datasets. In this research, a binary Salp Swarm Algorithm (SSA) with asynchronous updating rules and a new leadership structure is proposed. To set the best leadership structure, several extensive experiments are performed to determine the most effective number of leaders in the social organization of the artificial salp chain. Inspired from the behaviour of a termite colony (TC) in dividing the termites into four types, the salp chain is then divided into several sub-chains, where the salps in each sub-chain can follow a different strategy to adaptively update their locations. Three different updating strategies are employed in this paper. The proposed algorithm is tested and validated on 20 well-known datasets from the UCI repository. The results and comparisons verify that utilizing half of the salps as leaders of the chain can significantly improve the performance of SSA in terms of accuracy metric. Furthermore, dynamically tuning the single parameter of algorithm enable it to more effectively explore the search space in dealing with different feature selection datasets.

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“…This indicates that the parameter values are problem dependent. Many researchers tried to use some adaptive mechanisms to control the parameters in different optimizers [10,1,69].…”

Section: Related Workmentioning

confidence: 99%

Asynchronous accelerating multi-leader salp chains for feature selection

Aljarah

Mafarja

Heidari

et al. 2018

Applied Soft Computing

178

View full text Add to dashboard Cite

show abstract

“…Aladeemy et al [37] propose a variation of the cohort intelligence algorithm for feature selection. The efficiency of the proposed algorithm was compared to the well-known metaheuristics: Genetic Algorithm, Particle Swarm Optimization, Differential Evolution and Artificial Bee Colony.…”

Section: Feature Selectionmentioning

confidence: 99%

A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

et al. 2018

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This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric and asymmetric structure of a transfer function, which is responsible to map a continuous search space to a binary search space. A new method for design of a fuzzy-rule-based classifier using metaheuristics called Gravitational Search Algorithm (GSA) is discussed. The paper identifies three basic stages of the classifier construction: feature selection, creating of a fuzzy rule base and optimization of the antecedent parameters of rules. At the first stage, several feature subsets are obtained by using the wrapper scheme on the basis of the binary GSA. Creating fuzzy rules is a serious challenge in designing the fuzzy-rule-based classifier in the presence of high-dimensional data. The classifier structure is formed by the rule base generation algorithm by using minimum and maximum feature values. The optimal fuzzy-rule-based parameters are extracted from the training data using the continuous GSA. The classifier performance is tested on real-world KEEL (Knowledge Extraction based on Evolutionary Learning) datasets. The results demonstrate that highly accurate classifiers could be constructed with relatively few fuzzy rules and features.

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“…The architecture of the ANN consists of the input layer, hidden layer, and output layer, where each layer consists of one or more neurons [47]- [51]. The backpropagation method uses three steps to perform the training presented in feedforward of the training input pattern, backpropagation of connected error, and weight adjustment [28], [52]. In details, the ANN-Backpropagation architecture is shown in Figure 1.…”

Section: Problem Statementmentioning

confidence: 99%

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

Yuan¹,

Afandi²,

Putranto³

2018

EECSI

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Presently, electrical energy consumption continues to increase from year to year. Therefore, a short-term load forecasting is required that electricity providers can deliver continuous electrical energy to electricity consumers. By considering the estimation of the electrical load, the scheduling plan for operation and allocation of reserves can be managed well by the supply side. This study is focused on a forecasting of electrical loads using Artificial Neural Network (ANN) method considering a backpropagation algorithm model. The advantage of this method is to forecast the electrical load in accordance with patterns of past loads that have been taught. The data used for the learning is Actual Peak Load Period (APLP) data on the 150 kV system during 2017. Results show that the best network architecture is structured for the APLP Day and Night. Moreover, the momentum setting and understanding rate are 0.85 and 0.1 for the APLP Day. In contrast, 0.9 and 0.15 belong to the APLP Night. Based on the best network architecture, the APLP day testing process generates Mean Squared Error (MSE) around 0.04 and Mean Absolute Percentage Error (MAPE) around 4.66%, while the APLP Night generates MSE in 0.16 and MAPE in 16.83%.

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A new hybrid approach for feature selection and support vector machine model selection based on self-adaptive cohort intelligence

Cited by 59 publications

References 46 publications

Asynchronous accelerating multi-leader salp chains for feature selection

Asynchronous accelerating multi-leader salp chains for feature selection

A Fuzzy Classifier with Feature Selection Based on the Gravitational Search Algorithm

Demand Forecasting Considering Actual Peak Load Periods Using Artificial Neural Network

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