Dimensionality reduction in evolutionary algorithms-based feature selection for motor imagery brain-computer interface

Tan, Ping; Wang, Xin; Wang, Yong

doi:10.1016/j.swevo.2019.100597

Cited by 35 publications

(26 citation statements)

References 29 publications

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“…Tan et al [52] presented a feature selection method based on the evolutionary algorithm (EA) to reduce the dimensionality of motor imagery brain-computer interface from electroencephalogram (EEG) signals. The subset of important features was generated from each iteration of the EA, while the redundant and insignificant features were eliminated.…”

Section: A Feature Selectuion Methodsmentioning

confidence: 99%

“…Also, about 50% of the reviewed feature selection methods were based on the optimization approaches. Where these methods used several optimized algorithms such as PSO in [53] and [69], EA in [52], Ant in [58], GA in [64] and [70], CAF in [65] and [68] and deep learning in [61] and [65]. In [53] and [69] PSO algorithm was used to incorporate the features information into search space and hence selecting the most desired features and removing not required ones.…”

Section: Sellami and Farahmentioning

confidence: 99%

“…In [53] and [69] PSO algorithm was used to incorporate the features information into search space and hence selecting the most desired features and removing not required ones. In [52] EA was used to reduce the dimensionality of the search space by eliminating the unnecessary features from each iteration process, then influential features were selected at the same time. In [58] after the features clustered, the binary Ant used to rank them from each cluster.…”

Section: Sellami and Farahmentioning

confidence: 99%

See 2 more Smart Citations

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Section: A Feature Selectuion Methodsmentioning

confidence: 99%

Section: Sellami and Farahmentioning

confidence: 99%

Section: Sellami and Farahmentioning

confidence: 99%

See 1 more Smart Citation

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

607

210

View full text Add to dashboard Cite

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“…Many of the evolutionary algorithms lack this inherent attribute for accurate selection of features for deletion and reduction of the attributes. The results in the paper show that cuckoo search outperforms many of the existing and applied algorithms for dimensionality reduction phenomenon [54].…”

mentioning

confidence: 87%

Dimensionality Reduction for Internet of Things Using the Cuckoo Search Algorithm: Reduced Implications of Mesh Sensor Technologies

Yaseen

Nazir

Sabah

et al. 2020

Wireless Communications and Mobile Computing

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The internet of things is used as a demonstrative keyword for evolution of the internet and physical realms, by means of pervasive distributed commodities with embedded identification, sensing, and actuation abilities. Imminent intellectual technologies are subsidizing internet of things for information transmission within physical and autonomous digital entities to provide amended services, leading towards a new communication era. Substantial amounts of heterogeneous hardware devices, e.g., radio frequency identification (RFID) tags, sensors, and various network protocols are exploited to support object identification and network communication. Data generated by these digital objects is termed as “Big Data” and incorporates high dimensional space with noisy, irrelevant, and redundant features. Direct execution of mining techniques onto such kind of high dimensionality attribute space can increase cost and complexity. Data analytic mechanisms are embedded into internet of things to permit intelligent decision-making capabilities. These notions have raised new challenges regarding internet of things from a data and algorithm perspective. The proposed study identifies the problem in the internet of things network and proposes a novel cuckoo search-based outdoor data management. The technique of the feature extraction is used for the extraction of expedient information from raw and high-dimensional data. After the implementation for the cuckoo search-based feature extraction, few test benchmarks are introduced to evaluate the performance of mutated cuckoo search algorithms. The consequential low-dimensional data optimizes classification accuracy along with reduced complexity and cost.

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“…Feature selection methods select representative features from massive data and the generating optimized subsets can help improve the efficiency of computation in classification. Cutting down the irrelevant, redundant or the trivial features is the core of it [ 5 – 7 ]. Methods of feature selection can be classified into two main categories.…”

Section: Introductionmentioning

confidence: 99%

An Adapting Chemotaxis Bacterial Foraging Optimization Algorithm for Feature Selection in Classification

Hong

2020

Lecture Notes in Computer Science

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Efficient classification methods can improve the data quality or relevance to better optimize some Internet applications such as fast searching engine and accurate identification. However, in the big data era, difficulties and volumes of data processing increase drastically. To decrease the huge computational cost, heuristic algorithms have been used. In this paper, an Adapting Chemotaxis Bacterial Foraging Optimization (ACBFO) algorithm is proposed based on basic Bacterial Foraging Optimization (BFO) algorithm. The aim of this work is to design a modified algorithm which is more suitable for data classification. The proposed algorithm has two updating strategies and one structural changing. First, the adapting chemotaxis step updating strategy is responsible to increase the flexibility of searching. Second, the feature subsets updating strategy better combines the proposed heuristic algorithm with the KNN classifier. Third, the nesting structure of BFO has been simplified to reduce the computation complexity. The ACBFO has been compared with BFO, BFOLIW and BPSO by testing on 12 widely used benchmark datasets. The result shows that ACBFO has a good ability of solving classification problems and gets higher accuracy than the other comparation algorithm.

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Dimensionality reduction in evolutionary algorithms-based feature selection for motor imagery brain-computer interface

Cited by 35 publications

References 29 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Dimensionality Reduction for Internet of Things Using the Cuckoo Search Algorithm: Reduced Implications of Mesh Sensor Technologies

An Adapting Chemotaxis Bacterial Foraging Optimization Algorithm for Feature Selection in Classification

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