A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification

Tiwari, Anurag; Chaturvedi, Amrita

doi:10.1016/j.eswa.2022.116621

Cited by 54 publications

(24 citation statements)

References 46 publications

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“…Where 𝜒 𝐹 2 represents the chi-square value. Iman and Davenport show that Friedman's 𝜒 𝐹 2 is too conservative and come up with a better statistical formula, which is shown in equation (13).…”

Section: A Comparison Of Fafs_hfs With Other Feature Selection Methodsmentioning

confidence: 99%

“…For example, scholar Jain and Singh introduced a two-phase hybrid feature selection method based on principal component analysis (PCA), ReliefF and adaptive support vector machine (SVM) [12]. Tiwari proposed a mixed information theory with dynamic butterfly optimization algorithm [13]. Uzer et al proposed a hybrid feature selection method based on sequential forward selection (SFS), sequential backward selection (SBS) and PCA [14].…”

Section: Introductionmentioning

confidence: 99%

“…Ghosh P et al selected features by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO), and integrating the traditional classifiers with bagging and boosting methods in training process [18]. Tiwari A developed a new hybrid feature selection method, namely, the Iterative Feature Selection using Dynamic Butterfly Optimization Algorithm based Interaction Maximization (IFS-DBOIM) [19]. These hybrid feature selection methods can yield better classification ability than uses either approach alone.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Feature Selection Method Based on Feature Subset and Factor Analysis

et al. 2022

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With the advent of big data era and the rapid improvement of raw data scale, feature selection is the basic and critical technologies for data mining. However, in most of the studies on feature selection methods before, mainly directed to treat the single feature or overall feature subset, while the influence of the correlation and redundancy of features in the feature subset on the classification results is ignored. In this paper, combination of feature subset grouping and factor analysis (FA), a hybrid feature selection method based on feature subsets generation through factor analysis (FAFS_HFS) is proposed. Firstly, generate feature subsets based on the maximum load (maximum explanatory power) of each feature through factor analysis. Then, minimum redundancy maximum relevance (mRMR) and sequential forward selection (SFS) is used to remove the redundancy of each feature subset. Finally, fisher score (F-score) and feature subset SFS (FS_SFS) was utilized to evaluate and select feature subset, and obtain the optimal feature subset. Experiments are conducted on 14 datasets, the results show that FAFS_HFS has high classification accuracy and dimension reduction on almost all datasets, especially in high-dimensional datasets, and it has excellent efficiency and competitive classification performance compared with other contrastive methods.INDEX TERMS hybrid feature selection, feature subset, factor analysis (FA), mRMR, F-score, sequential forward selection (SFS)

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Section: A Comparison Of Fafs_hfs With Other Feature Selection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Feature Selection Method Based on Feature Subset and Factor Analysis

et al. 2022

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“…Recently developed original algorithms include forensic-based investigation algorithm (FBI) 8 , the slime mould algorithm (SMA) 11 , the group teaching optimization algorithm (GTOA) 12 , dynamic group optimization (DGO) 13 , the African vultures optimization algorithm (AVOA) 14 , the Rao-3 algorithm 15 , the gorilla troops optimizer (GTO) 16 , smell agent optimization (SAO) 17 , the sparrow search algorithm (SSA) 18 , the artificial ecosystem optimizer (AEO) 19 , the starling murmuration optimizer (SMO) 20 , the dwarf mongoose optimization algorithm (DMOA) 21 , the war strategy optimization algorithm (WSOA) 22 , the dynamic butterfly optimization algorithm (DBOA) 23 , the artificial hummingbird optimization technique (AHOT) 24 , and the antlion optimization algorithm (ALOA) 25 .…”

Section: Introductionmentioning

confidence: 99%

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Chou

Molla

2022

Sci Rep

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The complexity of engineering optimization problems is increasing. Classical gradient-based optimization algorithms are a mathematical means of solving complex problems whose ability to do so is limited. Metaheuristics have become more popular than exact methods for solving optimization problems because of their simplicity and the robustness of the results that they yield. Recently, population-based bio-inspired algorithms have been demonstrated to perform favorably in solving a wide range of optimization problems. The jellyfish search optimizer (JSO) is one such bio-inspired metaheuristic algorithm, which is based on the food-finding behavior of jellyfish in the ocean. According to the literature, JSO outperforms many well-known meta-heuristics in a wide range of benchmark functions and real-world applications. JSO can also be used in conjunction with other artificial intelligence-related techniques. The success of JSO in solving diverse optimization problems motivates the present comprehensive discussion of the latest findings related to JSO. This paper reviews various issues associated with JSO, such as its inspiration, variants, and applications, and will provide the latest developments and research findings concerning JSO. The systematic review contributes to the development of modified versions and the hybridization of JSO to improve upon the original JSO and present variants, and will help researchers to develop superior metaheuristic optimization algorithms with recommendations of add-on intelligent agents.

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“…Thus, finding a suitable set of predictors from the high-dimensional data, consist of physical factors, as the input of the runoff prediction model is still a challenge for medium and long-term runoff prediction. In information theory, mutual information (MI) method is a powerful tool for analyzing linear and nonlinear relationships between variables (Elkiran et al, 2021), but simply selecting predictors through correlation analysis provided by MI will introduce many redundant features (Tiwari and Chaturvedi 2022). To solve this problem, in traditional high-dimensional data preprocessing, principal component analysis (PCA) is typically used to reduce dimensionality and it can effectively reduce redundant variables and feature dimensions (Ouyang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Climate-informed monthly runoff prediction model using machine learning and feature importance analysis

Yan

Lei

Jiang

et al. 2022

Front. Environ. Sci.

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Accurate runoff prediction can provide a reliable decision-making basis for flood and drought disaster prevention and scientific allocation of water resources. Selecting appropriate predictors is an effective way to improve the accuracy of runoff prediction. However, the runoff process is influenced by numerous local and global hydrometeorological factors, and there is still no universal approach about the selection of suitable predictors from these factors. To address this problem, we proposed a runoff prediction model by combining machine learning (ML) and feature importance analysis (FIA-ML). Specifically, take the monthly runoff prediction of Yingluoxia, China as an example, the FIA-ML model uses mutual information (MI) and feature importance ranking method based on random forest (RF) to screen suitable predictors, from 130 global climate factors and several local hydrometeorological information, as the input of ML models, namely the hybrid kernel support vector machine (HKSVM), extreme learning machine (ELM), generalized regression neural network (GRNN), and multiple linear regression (MLR). An improved particle swarm optimization (IPSO) is used to estimate model parameters of ML. The results indicated that the performance of the FIA-ML is better than widely-used long short-term memory neural network (LSTM) and seasonal autoregressive integrated moving average (SARIMA). Particularly, the Nash-Sutcliffe Efficiency coefficients of the FIA-ML models with HKSVM and ELM were both greater than 0.9. More importantly, the FIA-ML models can explicitly explain which physical factors have significant impacts on runoff, thus strengthening the physical meaning of the runoff prediction model.

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A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification

Cited by 54 publications

References 46 publications

Hybrid Feature Selection Method Based on Feature Subset and Factor Analysis

Hybrid Feature Selection Method Based on Feature Subset and Factor Analysis

Recent advances in use of bio-inspired jellyfish search algorithm for solving optimization problems

Climate-informed monthly runoff prediction model using machine learning and feature importance analysis

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