A Comparative Study of Feature Selection Techniques for Bat Algorithm in Various Applications

Mohamed, Ramizi; Yusof, Munirah Mohd; Wahidi, Noorhaniza

doi:10.1051/matecconf/201815006006

Cited by 10 publications

(6 citation statements)

References 10 publications

(11 reference statements)

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“…Feature selection approaches can be divided into two categories: The search space is decomposed into four classes; they are: "exhaustive", "random", "heuristic", "meta-heuristic". And the other is the strategybased techniques which are decomposed into filter and wrapper feature selection approach [19][20][21][22][23].…”

Section: Feature Selection Methods Via Metaheuristicsmentioning

confidence: 99%

Feature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan Algorithm

Muhsen

Jumaa

Bakri

et al. 2021

Int. J. Interact. Mob. Technol.

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The task of network security is to keep services available at all times by dealing with hacker attacks. One of the mechanisms obtainable is the Intrusion Detection System (IDS) which is used to sense and classify any abnormal actions. Therefore, the IDS system should always be up-to-date with the latest hacker attack signatures to keep services confidential, safe, and available. IDS speed is a very important issue in addition to learning new attacks. A modified selection strategy based on features was proposed in this paper one of the important swarm intelligent algorithms is the Meerkat Clan Algorithm (MCA). Meerkat Clan Algorithm has good diversity solutions through its neighboring generation conduct and it was used to solve several problems. The proposed strategy benefitted from mutual information to increase the performance and decrease the consumed time. Two datasets (NSL-KDD & UNSW-NB15) for Network Intrusion Detection Systems (NIDS) have been used to verify the performance of the proposed algorithm. The experimental findings indicate that, compared to other approaches, the proposed algorithm produces good results in a minimum of time.

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Section: Feature Selection Methods Via Metaheuristicsmentioning

confidence: 99%

Feature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan Algorithm

Muhsen

Jumaa

Bakri

et al. 2021

Int. J. Interact. Mob. Technol.

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“…Feature selection is one of the stage for preprocessing the data through the identification and selection of a subset of F features from the original data of D features (F < D) without any transformation [57]. In the domain of supervised learning, feature Selection attempts to maximize the accuracy of the classifier, minimizing the related measurement costs by reducing irrelevant and possibly redundant features [5,40,45,26,46,68,35,37,50,1]. Feature selection reduces the complexity and the associated computational cost and improves the probability that a solution will be comprehensible and realistic.…”

Section: Feature Selectionmentioning

confidence: 99%

“…Feature Selection methods reduce the dimensionality of datasets by removing features that are considered as irrelevant or noisy for the learning task. This topic has received a lot of attention in machine learning and pattern recognition communities [4,46,68,35,37,50,1]. In any dataset, data can be seen as a collection of data points called instances.…”

Section: Introductionmentioning

confidence: 99%

Feature and instance selection through discriminant analysis criteria

Dornaika

Moujahid²

2022

Soft Comput

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Feature selection and instance selection are two data preprocessing methods widely used in data mining and pattern recognition. The main goal is to reduce the computational cost of many learning tasks. Recently, joint feature and instance selection has been approached by solving some global optimization problems using meta-heuristics. This approach is not only computationally expensive, but also does not exploit the fact that the data usually has a structured manifold implicitly hidden in the data and its labels.In this paper, we address joint feature and instance selection using scores derived from discriminant analysis theory. We present three approaches for joint feature and instance selection.The first scheme is a wrapper technique, while the other two schemes are filtering techniques.In the filtering approaches, the search process uses a genetic algorithm where the evaluation criterion is mainly given by the discriminant analysis score. This score depends simultaneously on the feature subset candidate and the best corresponding subset of instances. Thus, the best feature subset and the best instances are determined by finding the best score. The performance of the proposed approaches is quantified and studied using image classification with Nearest Neighbor and Support Vector Machine Classifiers. Experiments are conducted on five public image datasets. We compare the performance of our proposed methods with several state-ofthe-art methods. The experiments performed show the superiority of the proposed methods over several baseline methods.

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Section: Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature and Instance Selection Through Discriminant Analysis Criteria

Dornaika

Moujahid

2021

Preprint

View full text Add to dashboard Cite

Feature selection and instance selection are two data preprocessing methods widely used in data mining and pattern recognition. The main goal is to reduce the computational cost of many learning tasks. Recently, joint feature and instance selection has been approached by solving some global optimization problems using meta-heuristics. This approach is not only computationally expensive, but also does not exploit the fact that the data usually has a structured manifold implicitly hidden in the data and its labels. In this paper, we address joint feature and instance selection using scores derived from discriminant analysis theory. We present three approaches for joint feature and instance selection. The first scheme is a wrapper technique, while the other two schemes are filtering techniques. In the filtering approaches, the search process uses a genetic algorithm where the evaluation criterion is mainly given by the discriminant analysis score. This score depends simultaneously on the feature subset candidate and the best corresponding subset of instances. Thus, the best feature subset and the best instances are determined by finding the best score. The performance of the proposed approaches is quantified and studied using image classification with Nearest Neighbor and Support Vector Machine Classifiers. Experiments are conducted on five public image datasets. We compare the performance of our proposed methods with several state-of-the-art methods. The experiments performed show the superiority of the proposed methods over several baseline methods.

show abstract

A Comparative Study of Feature Selection Techniques for Bat Algorithm in Various Applications

Cited by 10 publications

References 10 publications

Feature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan Algorithm

Feature Selection Strategy for Network Intrusion Detection System (NIDS) Using Meerkat Clan Algorithm

Feature and instance selection through discriminant analysis criteria

Feature and Instance Selection Through Discriminant Analysis Criteria

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