Feature Selection Based on Modified Bat Algorithm

Yang, Bin; Lu, Yuliang; Zhu, Ke; Yang, Guozheng; Liu, Jingwei; Yin, Haibo

doi:10.1587/transinf.2016edp7471

Cited by 22 publications

(10 citation statements)

References 29 publications

(40 reference statements)

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“…The increased number of attack classes and its highly imbalanced records pose a significant challenge to every machine learning approach. In order to further evaluate our proposed IDS model, we compare it with some well-known feature selection methods, namely IG (Information Gain) [11], IGR (Information Gain Ratio) [58], GA (Genetic Algorithm) [65], PSO (Particle Swarm Optimization) [98], and MBAFS (Modified Bat Algorithm for Feature Selection) [93] by conducting experiments based on these three datasets. Likewise, in this comparative study we use the common metrics in the context of Acc, F-Measure, ADR, and FAR.…”

Section: Comparison With Other Feature Selection Methodsmentioning

confidence: 99%

Building an efficient intrusion detection system based on feature selection and ensemble classifier

et al. 2020

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A B S T R A C T Intrusion detection system (IDS) is one of extensively used techniques in a network topology to safeguard the integrity and availability of sensitive assets in the protected systems. Although many supervised and unsupervised learning approaches from the field of machine learning have been used to increase the efficacy of IDSs, it is still a problem for existing intrusion detection algorithms to achieve good performance. First, lots of redundant and irrelevant data in high-dimensional datasets interfere with the classification process of an IDS. Second, an individual classifier may not perform well in the detection of each type of attacks. Third, many models are built for stale datasets, making them less adaptable for novel attacks. Thus, we propose a new intrusion detection framework in this paper, and this framework is based on the feature selection and ensemble learning techniques. In the first step, a heuristic algorithm called CFS-BA is proposed for dimensionality reduction, which selects the optimal subset based on the correlation between features. Then, we introduce an ensemble approach that combines C4.5, Random Forest (RF), and Forest by Penalizing Attributes (Forest PA) algorithms. Finally, voting technique is used to combine the probability distributions of the base learners for attack recognition. The experimental results, using NSL-KDD, AWID, and CIC-IDS2017 datasets, reveal that the proposed CFS-BA-Ensemble method is able to exhibit better performance than other related and state of the art approaches under several metrics.

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Section: Comparison With Other Feature Selection Methodsmentioning

confidence: 99%

Building an efficient intrusion detection system based on feature selection and ensemble classifier

et al. 2020

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“…Bat algorithm was used for attributes selection. The results specified the suggested algorithm outperforms other algorithms [18]. Bat algorithm can be a valuable option to solve this problem for high dimensional data.…”

Section: Bat Algorithm For Feature Selectionmentioning

confidence: 83%

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Sagban

Marhoon

Alubady

2020

IJECE

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Rule-based classification in the field of health care using artificial intelligence provides solutions in decision-making problems involving different domains. An important challenge is providing access to good and fast health facilities. Cervical cancer is one of the most frequent causes of death in females. The diagnostic methods for cervical cancer used in health centers are costly and time-consuming. In this paper, bat algorithm for feature selection and ant colony optimization-based classification algorithm were applied on cervical cancer data set obtained from the repository of the University of California, Irvine to analyze the disease based on optimal features. The proposed algorithm outperforms other methods in terms of comprehensibility and obtains better results in terms of classification accuracy.

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“…Enache and Sgârciu [141] improved the version of BBA with different classifiers such as SVM and C4.5 and applied to NSL-KDD datasets for intrusion detection systems. Yang et al [142] modified BA to improve the diversity of the population of bats so that it made a good balance between exploration and exploitation and solve the feature selection problem. To classify the MR brain tumour image, Kaur et al [143] modified BA by combining Fisher and parameter-free bat algorithm for good exploration.…”

Section: B Swarm Intelligence Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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Feature Selection Based on Modified Bat Algorithm

Cited by 22 publications

References 29 publications

Building an efficient intrusion detection system based on feature selection and ensemble classifier

Building an efficient intrusion detection system based on feature selection and ensemble classifier

Hybrid bat-ant colony optimization algorithm for rule-based feature selection in health care

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

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