Pattern Matching based Classification using Ant Colony Optimization based Feature Selection

Sreeja, N. K.; Ashok, S.

doi:10.1016/j.asoc.2015.02.036

Cited by 44 publications

(22 citation statements)

References 42 publications

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“…Although the fitness function is defined, the value of α has not been determined. Usually, α is set to 0.9 [21] or 1.0 [31]. On the one hand, if the value of α is 1.0, the objective of the algorithm is to improve the classification accuracy without considering the number of selected features.…”

Section: Dataset and Experimental Settingmentioning

confidence: 99%

Feature Selection Based on Modified Bat Algorithm

Yang

Zhu

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYThe rapid development of information techniques has lead to more and more high-dimensional datasets, making classification more difficult. However, not all of the features are useful for classification, and some of these features may even cause low classification accuracy. Feature selection is a useful technique, which aims to reduce the dimensionality of datasets, for solving classification problems. In this paper, we propose a modified bat algorithm (BA) for feature selection, called MBAFS, using a SVM. Some mechanisms are designed for avoiding the premature convergence. On the one hand, in order to maintain the diversity of bats, they are guided by the combination of a random bat and the global best bat. On the other hand, to enhance the ability of escaping from local optimization, MBAFS employs one mutation mechanism while the algorithm trapped into local optima. Furthermore, the performance of MBAFS was tested on twelve benchmark datasets, and was compared with other BA based algorithms and some well-known BPSO based algorithms. Experimental results indicated that the proposed algorithm outperforms than other methods. Also, the comparison details showed that MBAFS is competitive in terms of computational time. key words: feature selection, wrapper model, bat algorithm, premature convergence, SVM

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Section: Dataset and Experimental Settingmentioning

confidence: 99%

Feature Selection Based on Modified Bat Algorithm

Yang

Zhu

et al. 2017

IEICE Trans. Inf. & Syst.

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“…To further test the performance of MDisABC, we compare MDis-ABC with two ACO based algorithms [72,73] published in 2015, which use the similar methodology for feature selection to this paper. According to the first study [72], four datasets, including Wine, Vehicle, German and Ionosphere are common with this paper, and the classification results of them for ACO based feature selection are 4.51%, 28.25%, 30.40% and 14.82%, respectively.…”

Section: Comparisons With Recent Aco Papersmentioning

confidence: 99%

Section: Comparisons With Recent Aco Papersmentioning

confidence: 99%

“…According to the first study [72], four datasets, including Wine, Vehicle, German and Ionosphere are common with this paper, and the classification results of them for ACO based feature selection are 4.51%, 28.25%, 30.40% and 14.82%, respectively. The results show that MDisABC performs better than ACO [72]. According to the other study [73] with an improved ACO algorithm, Wine and Vehicle datasets are common with this paper, and the classification results of them for an improved version of ACO are 3.10% and 24.70%, respectively.…”

Section: Comparisons With Recent Aco Papersmentioning

confidence: 99%

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A binary ABC algorithm based on advanced similarity scheme for feature selection

Hançer

Xue

Karaboğa

et al. 2015

Applied Soft Computing

153

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“…On the other hand, pattern matching is regarded as important research field in computer science [6]. As the best of our konwledge, pattern matching has been widely utilized in network intrusion detection/prevention systems, virus-anti devices and Anti-Spam applications and so on.…”

Section: Introductionmentioning

confidence: 99%

Study on the Pattern Matching Technology and Its Application in Network Security

Ma¹,

Zhang²,

Yang³

et al. 2015

2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)

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In this paper, we concentrate on the problem of network security protection, and we proposed a novel pattern matching algorithm to tackle this problem. Particularly, we focus on the problem SQL Injection, which is a typical network security problem, and it means a type of web application security vulnerability. Afterwards, a typical pattern matching based network security system is illustrated. Additionally, we define that a pattern is a set of specific ternary strings in the traffic which illustrates the probability of potential intrusion or abnormality of the network. Furthermore, a novel pattern matching algorithm is given. In the end, to demonstrate the effectiveness of our method, experiments are conducted to maker performance.

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Pattern Matching based Classification using Ant Colony Optimization based Feature Selection

Cited by 44 publications

References 42 publications

Feature Selection Based on Modified Bat Algorithm

Feature Selection Based on Modified Bat Algorithm

A binary ABC algorithm based on advanced similarity scheme for feature selection

Study on the Pattern Matching Technology and Its Application in Network Security

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