Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion

Zhang, Long; Shan, Linlin; Wang, Jianhua

doi:10.1007/s00521-016-2204-0

Cited by 51 publications

(23 citation statements)

References 32 publications

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“…The most common stopping condition is the achieved number of iterations (n_ITER), which is sometimes referred to as generations. In the analyzed articles, researchers used the following number of iterations, ordered from the most used ones to the least: 100 [33,[41][42][43], 50 [35,44,45], 500 [46], 200 [47], 70 [34], and 25 [36]. If a composed stopping condition is used, it is always combined with the number of iterations.…”

Section: Defined Stopping Conditionmentioning

confidence: 99%

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Swarm Intelligence Algorithms for Feature Selection: A Review

2018

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The increasingly rapid creation, sharing and exchange of information nowadays put researchers and data scientists ahead of a challenging task of data analysis and extracting relevant information out of data. To be able to learn from data, the dimensionality of the data should be reduced first. Feature selection (FS) can help to reduce the amount of data, but it is a very complex and computationally demanding task, especially in the case of high-dimensional datasets. Swarm intelligence (SI) has been proved as a technique which can solve NP-hard (Non-deterministic Polynomial time) computational problems. It is gaining popularity in solving different optimization problems and has been used successfully for FS in some applications. With the lack of comprehensive surveys in this field, it was our objective to fill the gap in coverage of SI algorithms for FS. We performed a comprehensive literature review of SI algorithms and provide a detailed overview of 64 different SI algorithms for FS, organized into eight major taxonomic categories. We propose a unified SI framework and use it to explain different approaches to FS. Different methods, techniques, and their settings are explained, which have been used for various FS aspects. The datasets used most frequently for the evaluation of SI algorithms for FS are presented, as well as the most common application areas. The guidelines on how to develop SI approaches for FS are provided to support researchers and analysts in their data mining tasks and endeavors while existing issues and open questions are being discussed. In this manner, using the proposed framework and the provided explanations, one should be able to design an SI approach to be used for a specific FS problem.

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Section: Defined Stopping Conditionmentioning

confidence: 99%

“…The researchers in [42] used sigmoid function sig ij for the creation of the probability vector in the FA algorithm. The final position update is done after that with the Sigmoid function (Equation 9)where the rand is defined in [0, 1].…”

Section: Update and Move Agentsmentioning

confidence: 99%

Swarm Intelligence Algorithms for Feature Selection: A Review

2018

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“…The recently proposed effective distance takes a view of the relationships within all samples [16]. In spite of the complicated structures of the relationships among the samples, the main idea of the effective distance is that the class of the samples can be determined by a group of closest/farthest distances, which can be derived from the connectivity matrix , in which (), named the connectivity coefficient, denotes the probability of the sample belonging to the sample ’s class.…”

Section: Methodsmentioning

confidence: 99%

“…In image processing, several classification methods use the distance method to measure the coherence between samples [16], which gives us an inspiration to classify the imaging targets into different classes with the envelope intensities from different frames. Euclidean distance is typically used in signal processing or machine learning between two samples [17, 18].…”

Section: Introductionmentioning

confidence: 99%

Sidelobe reduction for plane wave compounding with a limited frame number

Guo

Wang

et al. 2018

BioMed Eng OnLine

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BackgroundIn ultrasound plane wave imaging (PWI), image details are often blurred by the off-axis artefacts resulting from high sidelobe. Recently plane wave compounding (PWC) is proposed as a promising technique for the sidelobe suppression in the PWI. However, its high demand for the frame number results in an obvious frame rate loss, which is intolerable in the ultrafast imaging modality. To reduce the number of frames required for compounding, coherence in the compounding frames should be exploited.MethodsIn this paper, we propose a global effective distance-based sidelobe suppressing method for the PWC with a limited frame number, where the global effective distance is introduced to measure the inter-frame coherence. Specifically, the effective distance is firstly computed by using a sparse representation-based algorithm. Then, the sidelobe localization is carried out on the basis of the effective distance. Finally, the target-dependent weighting factor is adopted to suppress the sidelobe.ResultsTo assert the superiority of our proposed method, we compare the performances of different sidelobe reduction methods on both simulated and experimental PWC data. In case of 5 steering angles, our method shows a 19 dB reduction in the peak sidelobe level compared to the normal PWC in the point spread function test, and the contrast ratio is enhanced by more than 10% in both the simulation and phantom studies.ConclusionsConsequently, the proposed method is convinced to be a promising approach in enhancing the PWC image quality.

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“…Moreover, feature selection methods have been applied in the classification problems such as bioinformatics and signal processing [33]. Generally, a cost-based feature selection method is used to maximize the classification performance and minimize the classification cost associated with the features, which is a multi-objective optimization problem.…”

Section: Related Workmentioning

confidence: 99%

EFS-MI: an ensemble feature selection method for classification

Hoque

Singh

Bhattacharyya

2017

Complex Intell. Syst.

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Feature selection methods have been used in various applications of machine learning, bioinformatics, pattern recognition and network traffic analysis. In high dimensional datasets, due to redundant features and curse of dimensionality, a learning method takes significant amount of time and performance of the model decreases. To overcome these problems, we use feature selection technique to select a subset of relevant and non-redundant features. But, most feature selection methods are unstable in nature, i.e., for different training datasets, a feature selection method selects different subsets of features that yields different classification accuracy. In this paper, we provide an ensemble feature selection method using feature-class and feature-feature mutual information to select an optimal subset of features by combining multiple subsets of features. The method is validated using four classifiers viz., decision trees, random forests, KNN and SVM on fourteen UCI, five gene expression and two network datasets.

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Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion

Cited by 51 publications

References 32 publications

Swarm Intelligence Algorithms for Feature Selection: A Review

Swarm Intelligence Algorithms for Feature Selection: A Review

Sidelobe reduction for plane wave compounding with a limited frame number

EFS-MI: an ensemble feature selection method for classification

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