A multi-objective feature selection method based on bacterial foraging optimization

Niu, Ben; Yi, Wenjie; Tan, Lijing; Geng, Shuang; Wang, Hong

doi:10.1007/s11047-019-09754-6

Cited by 20 publications

(15 citation statements)

References 35 publications

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“…Te adaptive chemotaxis bacterial foraging optimization algorithm (ACBFO) [42], improved swarming and elimination-dispersal bacterial foraging optimization algorithm (ISEDBFO) [42], and multiobjective bacterial-inspired algorithm (MOBIFS) [48] are three recently proposed BFO variants for FS, which have good performances. ACBFO proposed an adaptive chemotaxis strategy, and ISEDBFO adopts a hyperbolic tangent function and a roulette technique to improve the search efects of BFO in FS.…”

Section: Comparison Methodsmentioning

confidence: 99%

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High-Dimensional Classification with Missing Labels

Wang

et al. 2023

International Journal of Intelligent Systems

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Feature selection is a crucial method for discovering relevant features in high-dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real-world problems. To address high-dimensional and label-missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label-missing problem, a k-nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm-based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers.

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Section: Comparison Methodsmentioning

confidence: 99%

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High-Dimensional Classification with Missing Labels

Wang

et al. 2023

International Journal of Intelligent Systems

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“…It has massive amounts of swimming and flipping actions [18]. In j th chemotaxis method, the motion of i th bacterium is provided as follows.…”

Section: Chemotaxismentioning

confidence: 99%

Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

Hilal¹,

ISSAOUI²,

Obayya³

et al. 2022

Computers, Materials &Amp; Continua

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The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence (XAI), a process that explains how prediction is done in AI models. Biomedical mental disorder, i.e., Autism Spectral Disorder (ASD) needs to be identified and classified at early stage itself in order to reduce health crisis. With this background, the current paper presents XAI-based ASD diagnosis (XAI-ASD) model to detect and classify ASD precisely. The proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization (BFO)-based Feature Selection (FS) technique. In addition, Whale Optimization Algorithm (WOA) with Deep Belief Network (DBN) model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of WOA. In order to ensure a better ASD diagnostic outcome, a series of simulation process was conducted on ASD dataset.

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“…[67] presented a survey about multi-Objective FS and their applications. While, [68] employed multi-objective FS for bacterial foraging optimization.…”

Section: Review Of Related Workmentioning

confidence: 99%

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

et al. 2021

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Classification tasks often include, among the large number of features to be processed in the datasets, many irrelevant and redundant ones, which can even decrease the efficiency of classifiers. Feature Selection (FS) is the most common preprocessing technique utilized to overcome the drawbacks of the high dimensionality of datasets and often has two conflicting objectives: The first function aims to maximize the classification performance or reduce the error rate of the classifier. In contrast, the second function is designed to minimize the number of features. However, the majority of wrapper FS techniques are developed for single-objective scenarios. Multi-verse optimizer (MVO) is considered as one of the well-regarded optimization approaches in recent years. In this paper, the binary multi-objective variant of MVO (MOMVO) is proposed to deal with feature selection tasks. The standard MOMVO suffers from local optima stagnation, so we propose an improved binary MOMVO to deal with this issue using the memory concept and personal best of the universes. The experimental results and comparisons indicate that the proposed binary MOMVO approach can effectively eliminate irrelevant and/or redundant features and maintain a minimum classification error rate when dealing with different datasets compared with the most popular feature selection techniques. Furthermore, the 14 benchmark datasets showed that the proposed approach outperforms the stat-of-art multi-objective optimization algorithms for feature selection.

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A multi-objective feature selection method based on bacterial foraging optimization

Cited by 20 publications

References 35 publications

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High-Dimensional Classification with Missing Labels

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High-Dimensional Classification with Missing Labels

Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

A Robust Multi-Objective Feature Selection Model Based on Local Neighborhood Multi-Verse Optimization

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