Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification

Wang, Ziqian; Gao, Shangce; Zhang, Yong; Guo, Lijun

doi:10.1016/j.knosys.2022.109874

Cited by 25 publications

(5 citation statements)

References 49 publications

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“…The results show that TSHFS-ACO is more suitable for high-dimensional feature selection. Aiming at the problems of lacking feature interaction and premature convergence in traditional ACO, Wang et al [22] proposed a new ACO, which included symmetric uncertainty.…”

Section: Related Workmentioning

confidence: 99%

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Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

Wang

et al. 2023

IET Control Theory & Appl

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Feature selection is a fundamental pre‐processing step in machine learning that aims to reduce the dimensionality of a dataset by selecting the most effective features from the original features. This process is regarded as a combinatorial optimization problem, and the grey wolf optimizer (GWO), a novel meta‐heuristic algorithm, has gained popularity in feature selection due to its fast convergence speed and easy implementation. In this paper, an improved binary GWO algorithm incorporating a novel Population Adaptation strategy called PA‐BGWO is proposed. The PA‐BGWO takes into account the characteristics of the feature selection problem and designs three strategies. The proposed strategy includes an adaptive individual update procedure to enhance the exploitation ability and accelerate convergence speed, a head wolf fine‐tuned mechanism to exert the impact on each independent feature of the objective function, and a filter‐based method ReliefF for calculating feature weights with dynamically adjusted mutation probabilities based on the ranking features to effectively escape from local optima. Experimental comparisons with several state‐of‐the‐art feature selection methods on 15 classification problems demonstrate that the proposed approach can select a small feature subset with higher classification accuracy in most cases.

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Section: Related Workmentioning

confidence: 99%

“…Aiming at the problems of lacking feature interaction and premature convergence in traditional ACO, Wang et al. [22] proposed a new ACO, which included symmetric uncertainty.…”

Section: Related Workmentioning

confidence: 99%

Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

Wang

et al. 2023

IET Control Theory & Appl

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“…to overcome its limitations. These algorithms include particle swarm optimization (PSO) (35,36), ant colony optimization (ACO) (37,38), whale optimization (WO) (39,40), and GWO (41). Feature selection approaches based on optimization algorithms have the ability to efficiently explore large search spaces and often yield results that closely approximate the global solution.…”

Section: Feature Selectionmentioning

confidence: 99%

A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm

Dehghan,

Azizi

2023

Jundishapur J Nat Pharm Prod

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Background: Breast cancer is the second leading cause of death in women. The advent of machine learning (ML) has opened up a world of possibilities for the discovery and formulation of drugs. It is an exciting development that could revolutionize the pharmaceutical industry. By leveraging ML algorithms, researchers can now identify disease-related targets with greater accuracy. Additionally, ML techniques can be used to predict the toxicity and pharmacokinetics of potential drug candidates. Objectives: The main purpose of ML techniques, such as feature selection (FS) and classification, is to develop a learning model based on datasets. Methods: This paper proposed a hybrid intelligent approach using a Binary Grey Wolf Optimization Algorithm and a Self-Organizing Fuzzy Logic Classifier (BGWO-SOF) for breast cancer diagnosis. The proposed FS approach can not only reduce the complexity of feature space but can also avoid overfitting and improve the learning process. The performance of this proposed approach was evaluated on the 10-fold cross-validation technique and the Wisconsin Diagnostic Breast Cancer dataset. Although the performance of breast cancer detection is highly dependent on classification accuracy, most good classification methods have an essential flaw in that they simply seek to maximize the accuracy of classification while ignoring the costs of misclassification among various categories. This is even more important in classification problems when the initial set of features is large. With such a large number of features, it is of special interest to search for a dependency between an optimal number of selected features and the accuracy of the classification model. Results: In experiments, standard performance evaluation metrics, including accuracy, F-measure, precision, sensitivity, and specificity, were performed. The evaluation results demonstrated that the BGWO-SOF approach achieves 99.70% accuracy and 99.66% F-measure, which outperforms other state-of-the-art methods. Conclusions: During the comparison of the results, it was observed that the proposed approach gives better or more competitive results than other state-of-the-art methods. By leveraging the power of ML algorithms and artificial intelligence (AI) and the findings of the current study, we can optimize the selection of natural pharmaceutical products for the treatment of breast cancer and maximize their efficacy.

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“…Numerous studies have demonstrated the robustness and adaptability of ACO in resolving FS problems. Wang et al [30] introduced a novel approach for FS, namely, the probabilistic sequence-based graphical representation ACO, incorporating symmetric uncertainty (SU) into the algorithm. Paniri et al [31] presented an innovative multi-label FS method based on ACO, which used both unsupervised and supervised heuristic functions to seek features with minimal redundancy and maximal correlation with class labels.…”

Section: Introductionmentioning

confidence: 99%

High-Dimensional Feature Selection Based on Improved Binary Ant Colony Optimization Combined with Hybrid Rice Optimization Algorithm

Zhou

et al. 2023

International Journal of Intelligent Systems

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In the realm of high-dimensional data analysis, numerous fields stand to benefit from its applications, including the biological and medical sectors that are crucial for computer-aided disease diagnosis and prediction systems. However, the presence of a significant number of redundant or irrelevant features can adversely affect system accuracy and real-time diagnosis efficiency. To mitigate this issue, this paper proposes two innovative wrapper feature selection (FS) methods that integrate the ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO). HRO is a recently developed metaheuristic that mimics the breeding process of the three-line hybrid rice, which is yet to be thoroughly explored in the context of solving high-dimensional FS problems. In the first hybridization, ACO is embedded as an evolutionary operator within HRO and updated alternately with it. In the second form of hybridization, two subpopulations evolve independently, sharing the local search results to assist individual updating. In the initial stage preceding hybridization, a problem-oriented heuristic factor assignment strategy based on the importance of the knee point feature is introduced to enhance the global search capability of ACO in identifying the smallest and most representative features. The performance of the proposed algorithms is evaluated on fourteen high-dimensional biomedical datasets and compared with other recently advanced FS methods. Experimental results suggest that the proposed methods are efficient and computationally robust, exhibiting superior performance compared to the other algorithms involved in this study.

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Symmetric uncertainty-incorporated probabilistic sequence-based ant colony optimization for feature selection in classification

Cited by 25 publications

References 49 publications

Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

Binary grey wolf optimizer with a novel population adaptation strategy for feature selection

A Hybrid Intelligent Approach to Breast Cancer Diagnosis and Treatment Using Grey Wolf Optimization Algorithm

High-Dimensional Feature Selection Based on Improved Binary Ant Colony Optimization Combined with Hybrid Rice Optimization Algorithm

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