BSO-FS: Bee Swarm Optimization for Feature Selection in Classification

Sadeg, Souhila; Hamdad, Leila; Benatchba, Karima; Habbas, Zineb

doi:10.1007/978-3-319-19258-1_33

Cited by 14 publications

(8 citation statements)

References 23 publications

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“…where t is the current iteration number and Max iter denotes the maximum iteration number. The location of each follower salp is updated by equation (8):…”

Section: Binary Salp Swarm Algorithm (Bssa)mentioning

confidence: 99%

“…Then, the salps' positions are updated using equations (6), and (8). The IBSSA (TCSSA3) works, as follows:…”

Section: Binary Salp Swarm Algorithm (Bssa)mentioning

confidence: 99%

“…Whereas, feeding ML techniques with a huge number of features often causes several challenges such as high computation complexity, overfitting, and low interpretability of the final trained model. This is due to the curse of dimensionality arising from the proportion between the number of features and the number of samples [8]- [10]. Hence, to improve the performance of crack detection systems, there is a need to resolve this problem.…”

Section: Introductionmentioning

confidence: 99%

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Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

2020

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The emerging technology of Structural Health Monitoring (SHM) paved the way for spotting and continuous tracking of structural damage. One of the major defects in historical structures is cracking, which represents an indicator of potential structural deterioration according to its severity. This paper presents a novel crack severity recognition system using a hybrid filter-wrapper with multi-objective optimization feature selection method. The proposed approach comprises two main components, namely, (1) feature extraction based on hand-crafted feature engineering and CNN-based deep feature learning and (2) feature selection using hybrid filter-wrapper with a multi-objective improved salp swarm optimization. The proposed approach is trained and validated by utilizing 10 representative UCI datasets and 4 datasets of crack images. The obtained experimental results show that the proposed system enhances the performance of crack severity recognition with ≈ 37% and ≈ 31% increase in recognition average accuracy and F-measure, respectively. Also, a reduction rate of ≈ 67% is achieved in the extracted feature set with all the tested datasets compared to the conventional classification approaches using the whole set of features. Moreover, the proposed approach outperforms other approaches with classical feature selection methods in terms of feature reduction rate and computational time. It is noticed that using VGG16 learned features outperforms using the fused hand-crafted features by 17.7%, 15.9%, and 23.5% for fine, moderate, and severe crack recognition, respectively. The significance of this paper is to investigate and highlight the impact of applying multi-feature dimensionality reduction through adopting hybrid filter-wrapper with multi-objective optimization methods for feature selection considering the case study of crack severity recognition for SHM.

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“…where t is the current iteration number and Max iter denotes the maximum iteration number. The location of each follower salp is updated by equation (8):…”

Section: Binary Salp Swarm Algorithm (Bssa)mentioning

confidence: 99%

“…Then, the salps' positions are updated using equations (6), and (8). The IBSSA (TCSSA3) works, as follows:…”

Section: Binary Salp Swarm Algorithm (Bssa)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

2020

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“…Among existing works on feature selection using the wrapper approach, metaheuristics based on swarm intelligence, such as ant colony optimization (ACO) [ 58 ], genetic algorithm (GA) [ 59 ], and particle swarm optimization (PSO) [ 60 ], have been shown to be very promising approaches. Inspired by the foraging behavior of natural bees, Sadeg et al [ 61 ] proposed a metaheuristic algorithm, BSO-FS, to solve the feature selection problem, and the results showed that it could select efficiently relevant features while improving the classification accuracy for some public datasets. On basis of BSO-FS, a hybrid metaheuristic, QBSO-FS, was presented to make the search process more efficient and adaptive by integrating Q-learning with BSO, which gave very satisfactory results compared to recently published algorithms [ 62 ].…”

Section: Related Workmentioning

confidence: 99%

Enhanced Human Activity Recognition Using Wearable Sensors via a Hybrid Feature Selection Method

Fan

Gao

2021

Sensors

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The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.

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“…The review enables us to expose the best EC algorithms for optimal feature selection and the future research directions in FS. Sadeg et al [15] developed Bee Swarm Optimization (BSO) algorithm for FS. The proposed algorithm is wrapper based FS with classifier algorithm.…”

Section: Introductionmentioning

confidence: 99%

Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

Devi¹,

Sasikala²

2019

IJRTE

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The optimal feature subset selection over very high dimensional data is a vital issue. Even though the optimal features are selected, the classification of those selected features becomes a key complicated task. In order to handle these problems, a novel, Accelerated Simulated Annealing and Mutation Operator (ASAMO) feature selection algorithm is suggested in this work. For solving the classification problem, the Fuzzy Minimal Consistent Class Subset Coverage (FMCCSC) problem is introduced. In FMCCSC, consistent subset is combined with the K-Nearest Neighbour (KNN) classifier known as FMCCSC-KNN classifier. The two data sets Dorothea and Madelon from UCI machine repository are experimented for optimal feature selection and classification. The experimental results substantiate the efficiency of proposed ASAMO with FMCCSC-KNN classifier compared to Particle Swarm Optimization (PSO) and Accelerated PSO feature selection algorithms.

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BSO-FS: Bee Swarm Optimization for Feature Selection in Classification

Cited by 14 publications

References 23 publications

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

Using Hybrid Filter-Wrapper Feature Selection With Multi-Objective Improved-Salp Optimization for Crack Severity Recognition

Enhanced Human Activity Recognition Using Wearable Sensors via a Hybrid Feature Selection Method

Accelerated Simulated Annealing and Mutation Operator Feature Selection method for Big Data

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