A survey on swarm intelligence approaches to feature selection in data mining

Nguyen, Bach Hoai; Xue, Bing; Zhang, Mengjie

doi:10.1016/j.swevo.2020.100663

Cited by 309 publications

(109 citation statements)

References 101 publications

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“…In the following, each upperlevel solution (feature subset represented by a binary vector indicating selected features where "1" means that the corresponding feature is selected and "0" otherwise) is associated with an optimal lower-level solution (tree encoding a constructed feature that represents optimal combinations of feature subset). Both leader objectives could be conflicting as the minimization of the number of features may increase the classification-error-rate due to the removal of relevant features [60]. A similar observation could be seen for the follower objectives as maximizing the relevance (correlation with class labels) may increase the redundancy between the constructed features of the considered combination tree.…”

Section: Case Study: Multi-objective Bi-level Feature Constructionmentioning

confidence: 84%

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

et al. 2020

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Many decision making situations are characterized by a hierarchical structure where a lower-level (follower) optimization problem appears as a constraint of the upper-level (leader) one. Such kind of situations is usually modeled as a BLOP (Bi-Level Optimization Problem). The resolution of the latter usually has a heavy computational cost because the evaluation of a single upper-level solution requires finding its corresponding (near) optimal lower-level one. When several objectives are optimized in each level, the BLOP becomes a multi-objective task and more computationally costly as the optimum corresponds to a whole non-dominated solution set, called the PF (Pareto Front). Despite the considerable number of recent works in multi-objective evolutionary bi-level optimization, the number of methods that could be applied to the combinatorial (discrete) case is much reduced. Motivated by this observation, we propose in this paper an Indicator-Based version of our recently proposed Co-Evolutionary Migration-Based Algorithm (CEMBA), that we name IB-CEMBA, to solve combinatorial multi-objective BLOPs. The indicator-based search choice is justified by two arguments. On the one hand, it allows selecting the solution having the maximal marginal contribution in terms of the performance indicator from the lowerlevel PF. On the other hand, it encourages both convergence and diversity at the upper-level. The comparative experimental study reveals the outperformance of IB-CEMBA on a multi-objective bi-level productiondistribution problem. From the effectiveness viewpoint, the upper-level hyper-volume values and inverted generational distance ones vary in the intervals [0.8500, 0.9710] and [0.0072, 0.2420], respectively. From the efficiency viewpoint, IB-CEMBA has a good reduction rate of the Number of Function Evaluations (NFEs), lying in the interval [30.13%, 54.09%]. To further show the versatility of our algorithm, we have developed a case study in machine learning, and more specifically we have addressed the bi-level multiobjective feature construction problem.

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Section: Case Study: Multi-objective Bi-level Feature Constructionmentioning

confidence: 84%

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

et al. 2020

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“…As an emerging computing technology, swarm intelligence algorithm has attracted much attention due to its simplicity and global search capability [22] . Shao et al proposed a method based on combining Particle Swarm Optimization (PSO) to optimize the number of the hidden layer neurons, learning rate and the momentum in DBN, which was applied to analyze experimental signal of the rolling bearings, and PSO-DBN could recognize fault state more intuitively and accurately [23] .…”

Section: On This Basis Azami Et Al Proposed Refined Compositementioning

confidence: 99%

Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks

Luo

Zhou

et al. 2021

IEEE Access

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Rolling bearings, as the main components of the large industrial rotating equipment, usually work under complex conditions and are prone to break down. It can provide a certain theoretical basis for identifying the sub-health state of the industrial equipment by the analysis from the incipient weak signals. Thus, a subhealth recognition offline algorithm based on Refined Composite Multiscale Dispersion Entropy (RCMDE) and Deep Belief Network-Extreme Learning Machine (DBN-ELM) optimized by Improved Firework Algorithm (IFWA) is proposed. First of all, in light of the drawbacks that it is easy to fall into local optima and cross the boundary for exploding fireworks in Firework Algorithm (FWA), Cauchy mutation and adaptive dynamic explosion radius factor coefficient is introduced into IFWA. Secondly, Maximum Correlation Kurtosis Deconvolution (MCKD) optimized by the improved parameters is used to process the incipient vibration signals with nonlinearity, nonstationary, and IFWA is used to adaptively adjust to the period and the filter length in MCKD(IFWA-MCKD). Then, each sequence of signals is further extracted the feature-RCMDE to rich sample diversity. Finally, combining the powerful unsupervised learning capability from DBN and the generalization capability from ELM, DBN-ELM can be established. What's more, in order to avoid the interference of human on the parameters, IFWA is used to optimize the number of hidden nodes in DBN-ELM, and the IFWA-DBN-ELM is established. It shows that the algorithm has the higher sub-health recognition accuracy, better robustness and generalization, which has a better industrial application prospect.

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“…The population-based metaheuristicoptimization algorithm (Nguyen et al, 2020)known as Particle Swarm Optimization (PSO) algorithm is proposed by Kennedy and Eberhart(1995). PSO simulates the movement of birds thatare randomly looking for food in search space.…”

Section: B Optimization Algorithmmentioning

confidence: 99%

“…Xin-She ( 2010) was present Bat Algorithm (BA) (Nguyen et al, 2020). BA was developed that mimic the behavior of bat where according toecho to find the pray.…”

Section: B Optimization Algorithmmentioning

confidence: 99%

An Optimized DiscretizationApproach using k-Means Bat Algorithm

et.al¹

2021

TURCOMAT

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This study has proposed arelatively new discretization approach using k-means and Bat algorithm in preparation phase of classification problem. In essence, bat algorithm is applied to find the best search space solution. Eventually, the best search space solution is utilized to produce cluster centroid. The cluster centroid is very useful to determine appropriate breakpoint for discretization. The proposed discretization approach is applied in the experiments with continuous datasets. Decision Tree, k-Nearest Neighbours and Naïve Bayes classifiers are used in the experiments. The proposed discretization approach is evaluated against other existing approaches: K-Means algorithm, hybrid K-Means with Particle Swarm Optimization (PSO) and hybrid K-Means with Whale Optimization Algorithm (WOA).The classification performance is evaluated in terms of accuracy, recall, f-measure and receiver operating characteristic curve (ROC). To test the performance of the proposed algorithm, nine benchmark continuous datasets are used. The proposed algorithm show the better results compare to other approaches. The proposed algorithm performs better in discretization to solve classification problems.

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A survey on swarm intelligence approaches to feature selection in data mining

Cited by 309 publications

References 101 publications

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

Solving Combinatorial Multi-Objective Bi-Level Optimization Problems Using Multiple Populations and Migration Schemes

Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks

An Optimized DiscretizationApproach using k-Means Bat Algorithm

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