Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data

Song, Xianfeng; Zhang, Yong; Guo, Yinan; Sun, Xiaoyan; Wang, Yongli

doi:10.1109/tevc.2020.2968743

Cited by 225 publications

(85 citation statements)

References 56 publications

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“…Simply speaking, exploration is the process where solutions visit the un-visited spaces in anticipation of finding better solution, and exploitation means to plough a neighboring space effectively to discover more appropriate solution. Some of meta-heuristic cost based Feature Algorithms are proposed in [16], [17], [18], [19] . FS is considered as an optimization problem where we try to find the optimal set of relevant features [20].…”

Section: Introductionmentioning

confidence: 99%

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

et al. 2020

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Selecting the most relevant features from a high dimensional dataset is always a challenging task. In this regard, the feature selection (FS) method acts as a solution to this problem mainly in the domain of data mining and machine learning. It aims at improving the performance of a learning model greatly by choosing the relevant features and ignoring the redundant ones. Besides, this also helps to achieve efficient use of space and time by the learning model under consideration. Though over the years, many metaheuristic algorithms have been proposed by the researchers to solve FS problem, still this is considered as the open research problem due to its enormous challenges. Particularly, these algorithms, at times, suffer from poor convergence because of the improper tuning of exploration and exploitation phases. Here lies the importance of the hybrid meta-heuristics which help to improve the searching capability and convergence rate of the parent algorithms. To this end, the present work introduces a new hybrid meta-heuristic FS model by combining two meta-heuristics-Harmony Search (HS) algorithm and Artificial Electric Field Algorithm (AEFA), which we have named as Electrical Harmony based Hybrid Meta-heurtistic (EHHM). The proposed hybrid meta-heuristic converges faster than its predecessors, thereby ensuring its capability to search efficiently. Usability of EHHM is examined by applying it on 18 standard UCI datasets. Moreover, to prove its supremacy, we have compared it with 10 state-of-the-art FS methods. Link to code implementation of proposed method: khalid0007/Metaheuristic-Algorithms/FS_AEFAhHS.

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Section: Introductionmentioning

confidence: 99%

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

et al. 2020

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“…As a wrapper-based meta-heuristic feature selection algorithm for optimal/highly discriminative feature selection, the BPSO helps minimize (if not eliminate) the curse of dimensionality caused by high-dimensional features; however, since the past decade when the canonical PSO was introduced, more than 4000 variants of the PSO algorithm has been developed including but not limited to the multiobjective PSO for cost-based feature selection in classification [39], variable-size cooperative co-evolutionary PSO for feature selection on high-dimensional data [40], Quantum PSO [41], Comprehensive Learning PSO [42], etc. Also, its capabilities as a filter-based feature selection approach has been recently explored by the authors of [43] who introduced the novel filter-based bare-bone particle swarm optimization(FBPSO) algorithm for unsupervised feature selection in cases of unlabelled data.…”

Section: B Meta-heuristic Methods For Feature Selectionmentioning

confidence: 99%

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

Akpudo

Hur

2020

IEEE Access

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Accurate condition monitoring of industrial cyber-physical systems/components demands the use of reliable fault detection and isolation (FD&I) methodologies. Meta-heuristic algorithms for feature selection have good exploration capability for optimal discriminative feature selection for fault isolation/classification of which the Binary Particle swarm optimization (BPSO) is superior to its counterparts. This study presents a robust approach for vibration-based failure diagnostics of electromagnetic/solenoid pumps which employ a multi-domain feature extraction procedure (statistical time-domain and frequencydomain features, Mel frequency cepstral coefficients, and continuous wavelet coefficients) for capturing linear and nonlinear properties from the signals. Compared with other filter and wrapper methods for supervised feature selection, a hybrid filter-wrapper (Pearson's correlation-BPSO (ρ-BPSO)) feature selection procedure is proposed for global search of optimal discriminative (uncorrelated) features for fault diagnosis with an RBF-kernel support vector machine (SVM*). Subsequently, a practical case study involving five VSC63A5 solenoid pumps at various operating/fault conditions is presented for validating the performance of the proposed approach. Results show the superior performance of the proposed hybrid filterwrapper approach against filter-based and wrapper-based techniques for discriminative feature selection. Also, the proposed ρ-BPSO-SVM* diagnostics model performance was compared with other standard fault isolation/classification methods.

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“…Filter [17] T-test [18], information theory [19], Distributed FS using SC [20] Wrapper [21] SVM [22], k-NN [23] Embedded [24] LASSO [25], Gradient boosting [26] Evolutionary computation EA [27] GA [28], parallel GA [29], GP [30] CEA [31] CCEA [32] Swarm optimization [28] PSO [28], ACO [33] Hybrid TLBO+GSA [34], CMIM+BGA [35], mRMR-TLBOL [36] Others DE [37], MA [38], ABC [37]…”

Section: Evaluation Criteriamentioning

confidence: 99%

A Novel Penalty-Based Wrapper Objective Function for Feature Selection in Big Data Using Cooperative Co-Evolution

et al. 2020

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The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even with a higher number of features. Feature selection has two purposes: reducing the number of features to decrease computations and improving classification accuracy, which are contradictory but can be achieved using a single objective function. For this very purpose, this paper proposes a penalty-based wrapper objective function. This function can be used to evaluate the FS process using CCEA, hence called Cooperative Co-Evolutionary Algorithm-Based Feature Selection (CCEAFS). An experiment was performed using six widely used classifiers on six different datasets from the UCI ML repository with FS and without FS. The experimental results indicate that the proposed objective function is efficient at reducing the number of features in the final feature subset without significantly reducing classification accuracy. Based on different performance measures, in most cases, naïve Bayes outperforms other classifiers when using CCEAFS.

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Variable-Size Cooperative Coevolutionary Particle Swarm Optimization for Feature Selection on High-Dimensional Data

Cited by 225 publications

References 56 publications

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

EHHM: Electrical Harmony Based Hybrid Meta-Heuristic for Feature Selection

A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features

A Novel Penalty-Based Wrapper Objective Function for Feature Selection in Big Data Using Cooperative Co-Evolution

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