A hybrid feature selection approach based on improved PSO and filter approaches for image steganalysis

Chhikara, Rita; Sharma, Prabha; Singh, Latika

doi:10.1007/s13042-015-0448-0

Cited by 59 publications

(17 citation statements)

References 26 publications

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“…Chhikara et al [ 31 ] described a hybrid filter-wrapper feature selection algorithm based on improved particle swarm optimization. Multiple regression filtering techniques and t-tests were used to optimize the selection of key features.…”

Section: Methodsmentioning

confidence: 99%

Predicting human body composition using a modified adaptive genetic algorithm with a novel selection operator

et al. 2020

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Background Changes to human body composition reflect changes in health status to some extent. It has been recognized that these changes occur earlier than diseases. This means that a reasonable prediction of body composition helps to improve model users’ health. To overcome the low accuracy and poor adaptability of existing human body composition prediction models and obtain higher efficiency, we proposed a novel method for predicting human body composition which uses a modified adaptive genetic algorithm (MAGA). Methods Firstly, since there are many parameters for a human body composition model, and these parameters are associated, we designed a new parameter selection approach by combining the improved RReliefF method with the mRMR method. Following this, selected parameters were used to establish a model that fits body composition. Secondly, in order to accurately calculate the weight of parameters in this model, we proposed a solution which used a modified adaptive genetic algorithm, taking advantage of both roulette and optimum reservation strategies. This solution also has an improved selection operator. Thirdly, taking the percentage of body fat (PBF) as an example of body composition, we conducted experiments to compare performance between our algorithm and other algorithms. Results Through our simulations, we demonstrated that the adaptability of the proposed model is 0.9921, the mean relative error is 0.05%, the mean square error is 1.3 and the correlation coefficient is 0.982. When compared with the indexes of other models, our model has the highest adaptability and the smallest error. Additionally, the suggested model, which has a training time of 28.58s and a running time of 2.84s, is faster than some models. Conclusion The PBF prediction model established by MAGA has high accuracy, stronger generalization ability and higher efficiency, which could provide a new method for human composition prediction.

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Section: Methodsmentioning

confidence: 99%

Predicting human body composition using a modified adaptive genetic algorithm with a novel selection operator

et al. 2020

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“…Qi et al [46] use PSO with mutation mechanism and the SVM for feature selection in hyperspectral classification. Chhikara et al [47] propose a feature selection approach based on an improved PSO algorithm and filter approaches to enhance the classification accuracy and reduce the computational complexity in image steganalysis. The authors in [48] utilizes a filter-based feature selection technique which uses an information theoretic-PSO approach to determine the most optimal feature combination in biomedical entity extraction.…”

Section: Related Workmentioning

confidence: 99%

Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

Sun

et al. 2020

IEEE Access

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Feature selection is an effective approach to reduce the number of features of data, which enhances the performance of classification in machine learning. In this paper, we formulate a joint feature selection problem to reduce the number of the selected features while enhancing the accuracy. An improved binary particle swarm optimization (IBPSO) algorithm is proposed to solve the formulated problem. IBPSO introduces a local search factor based on Lévy flight, a global search factor based on weighting inertia coefficient, a population diversity improvement factor based on mutation mechanism and a binary mechanism to improve the performance of conventional PSO and to make it suitable for the binary feature selection problems. Experiments based on 16 classical datasets are selected to test the effectiveness of the proposed IBPSO algorithm, and the results demonstrate that IBPSO has better performance than some other comparison algorithms.

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“…These algorithms have realworld applications in the context of dimension reduction [2,3,4,5,6,7,8,9,10], interdependent smart city infrastructures [11,12], optimization [13,14,15]. This chapter focuses on application of aforementioned evolutionary algorithms in dimension reductions [16,17,18,19,20,21,22,23,24,25]. As mentioned in [1], some studies deployed GA, PSO, or their combination as effective tools for solving large-scale optimization problems, including optimal allocation of electric vehicle charging station and distributed renewable resource in power distribution networks [26,27], resource optimization in construction projects [15], and allocation of electric vehicle parking lots in smart grids [28].…”

Section: Overviewmentioning

confidence: 99%

“…The process of feature selection using RISAB• Application of particle swarm optimization in feature selection:Chhikara et. al[20] proposed HYBRID, a new approach using PSO for solving CoD problem in image steganalysis. They proposed a combined filter and wrapper based feature selection approach to deal with high domensionality problem in image steganalysis.…”

mentioning

confidence: 99%

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

Mohammadi

Amini

Arabnia

2020

Advances in Intelligent Systems and Computing

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In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach.Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis.

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A hybrid feature selection approach based on improved PSO and filter approaches for image steganalysis

Cited by 59 publications

References 26 publications

Predicting human body composition using a modified adaptive genetic algorithm with a novel selection operator

Predicting human body composition using a modified adaptive genetic algorithm with a novel selection operator

Bio-Inspired Feature Selection: An Improved Binary Particle Swarm Optimization Approach

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

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