Optimised approach of feature selection based on genetic and binary state transition algorithm in the classification of bearing fault in BLDC motor

Lee, Chun‐Yao; Le, Truong-An

doi:10.1049/iet-epa.2020.0168

Cited by 14 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In biomedical classification, 5 of 34 [45] introduced a hybrid FS technique merging the Binary State Transition Algorithm (BSTA) with the Relief filter approach, demonstrating its effectiveness in detecting bearing faults in brushless DC motors. Additionally, [46] combined envelope analysis and Hilbert-Huang transformation for FE from Hall sensor signals, highlighting the versatility of meta-heuristic approaches across a variety set of applications in different domains.…”

Section: Meta-heuristic Algorithms For Feature Selectionmentioning

confidence: 99%

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Al-Eiadeh,

Qaddoura,

Abdallah

2024

Preprint

View full text Add to dashboard Cite

High dimensional datasets are highly likely to have redundant, irrelevant, and noisy features that negatively affect the performance of the classification algorithms. Selecting the most relevant features and reducing the dimensions of datasets by removing the undesired features is a dimensional reduction technique called Feature Selection (FS). In this paper, we propose an FS approach based on the Black Hole Algorithms (BHO) with a mutation technique called MBHO. Generally, BHO contains two major phases. At the exploitation phase, a set of stars are modified based on some rule and according to some objective function, the best star is selected as the black hole which attracts other stars. Furthermore, when a star gets closer to the event horizon, it will be swallowed and a new one will be randomly generated in the search space which thus is the exploration phase. However, randomness may cause the algorithm to fall into the trap of local optima, and to overcome such complications, inversion mutation is used. Furthermore, we modify a widely utilized objective function in most of the proposed works for wrapper feature selection by combining two new terms that are based on the correlation among the selected subset of features and the features and the classification label. We also utilize a transfer function, known as the V2 transfer function, to convert continuous values into discrete ones to enhance search. We assess our approach via extensive evaluation experiments using fourteen benchmark datasets. We benchmark the performance of a wrapper FS approach called Binary Cuckoo Search (BCS), and three filter-based FS approaches (namely Mutual Information Maximisation (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR)). Our evaluation has shown that the proposed model is an effective approach for FS, in selecting better features that enhance the performance metrics on the classifiers. Thus, MBHO can be utilized as one alternative to the existing state-of-art-approaches. We release the source codes of our implementation for the community to build on with new methods and datasets.

show abstract

Section: Meta-heuristic Algorithms For Feature Selectionmentioning

confidence: 99%

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Al-Eiadeh,

Qaddoura,

Abdallah

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Therefore, when extracting text features, the feature words in the same text can be put into a feature vector representing the text, so as to avoid ignoring the connection between feature items [15][16]. On this basis, this paper proposes a text feature vector on the basic of X 2 statistics, which can not only preserve the correlation between text features, but also distinguish the correlation between features and classes; and uses this vector as the initial population, through multiple rounds of genetic vectors are obtained to improve classification accuracy; through the coordination of crossover operation and mutation operation, global search can be realized and local minima can be avoided [17][18]; according to the characteristics of feature extraction, the fitness function and intersection rules are designed to solve the problem of inappropriate processing of low-frequency words in statistical analysis [19][20]. The flow chart of feature extraction on the basic of GA is shown in Figure 2: The figure 3 shows the average fitness of the population optimized by the GA has reached above 0.935, and these data show that the fitness of individuals in the population is better, and the effect of evolution is better.…”

Section: Feature Extraction Technology On the Basic Of Genetic Algorithmmentioning

confidence: 99%

Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm

Sun

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

View full text Add to dashboard Cite

Project bidding is a widely used method of project transactions in the world. It promotes fairness and justice in the field of project construction, and also prevents and overcomes corruption in the field of project construction. Nowadays, electronic bidding has gradually replaced traditional bidding methods, and the big date (BD) brought by electronic bidding has also provided sufficient convenience for the establishment and improvement of engineering enterprise credit evaluation systems. Therefore, this paper is to solve the problem of insufficient application of BD in the current engineering electronic bidding. This paper combines the TCM-KNN classification algorithm with the genetic algorithm(GA), collects data information related to bidding transactions from multiple channels, and conducts research on the internal correlation and mutual influence between these data information, so as to determine the evaluation indicators suitable for engineering enterprise credit evaluation. And on the basic of the BD analysis of engineering electronic bidding, this paper establishes the corresponding engineering enterprise credit evaluation(ECE) index system and ECE model. Finally, the experimental results show that the GA is used to optimize the TCM-KNN model, and the parameters that are more in line with the actual situation are obtained.

show abstract

“…Furthermore, Ref. [46] combined envelope analysis and Hilbert-Huang transformation for feature extraction from Hall sensor signals, showcasing the versatility of meta-heuristic approaches across diverse applications in various domains.…”

Section: Meta-heuristic Algorithms For Feature Selectionmentioning

confidence: 99%

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Al-Eiadeh,

Qaddoura,

Abdallah

2024

Applied Sciences

View full text Add to dashboard Cite

High-dimensional datasets often harbor redundant, irrelevant, and noisy features that detrimentally impact classification algorithm performance. Feature selection (FS) aims to mitigate this issue by identifying and retaining only the most pertinent features, thus reducing dataset dimensions. In this study, we propose an FS approach based on black hole algorithms (BHOs) augmented with a mutation technique termed MBHO. BHO typically comprises two primary phases. During the exploration phase, a set of stars is iteratively modified based on existing solutions, with the best star selected as the “black hole”. In the exploration phase, stars nearing the event horizon are replaced, preventing the algorithm from being trapped in local optima. To address the potential randomness-induced challenges, we introduce inversion mutation. Moreover, we enhance a widely used objective function for wrapper feature selection by integrating two new terms based on the correlation among selected features and between features and classification labels. Additionally, we employ a transfer function, the V2 transfer function, to convert continuous values into discrete ones, thereby enhancing the search process. Our approach undergoes rigorous evaluation experiments using fourteen benchmark datasets, and it is compared favorably against Binary Cuckoo Search (BCS), Mutual Information Maximization (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Eelevance (mRMR), approaches. The results demonstrate the efficacy of our proposed model in selecting superior features that enhance classifier performance metrics. Thus, MBHO is presented as a viable alternative to the existing state-of-the-art approaches. We make our implementation source code available for community use and further development.

show abstract

Optimised approach of feature selection based on genetic and binary state transition algorithm in the classification of bearing fault in BLDC motor

Cited by 14 publications

References 37 publications

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Platform Design of Project Bidding System on the Basic of TCM-KNN and Genetic Algorithm

Investigating the Performance of a Novel Modified Binary Black Hole Optimization Algorithm for Enhancing Feature Selection

Contact Info

Product

Resources

About