Multi-objective Feature Selection with NSGA II

Hamdani, Tarek M.; Won, Jin-Myung; Alimi, Adel M.; Karray, Fakhri

doi:10.1007/978-3-540-71618-1_27

Cited by 125 publications

(66 citation statements)

References 14 publications

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“…50 This method is designed to select the more informative features for image quality from all aesthetic features. The goal is to find an optimal subset of features by minimizing the number of used features and quality prediction error.…”

Section: Review On Related Measuresmentioning

confidence: 99%

“…Then, we utilized the NSGA-II feature selection method 50 to select the more informative features for image quality from the augmented feature vector. This method is designed to find an optimal subset of features by minimization of both the number of used features and the classification error using the 1-NN classifier.…”

Section: Optimal Feature Selection Analysismentioning

confidence: 99%

See 1 more Smart Citation

Blind image quality assessment based on aesthetic and statistical quality-aware features

Jenadeleh

Masaeli

Moghaddam

2017

J. Electron. Imaging

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Abstract. The main goal of image quality assessment (IQA) methods is the emulation of human perceptual image quality judgments. Therefore, the correlation between objective scores of these methods with human perceptual scores is considered as their performance metric. Human judgment of the image quality implicitly includes many factors when assessing perceptual image qualities such as aesthetics, semantics, context, and various types of visual distortions. The main idea of this paper is to use a host of features that are commonly employed in image aesthetics assessment in order to improve blind image quality assessment (BIQA) methods accuracy. We propose an approach that enriches the features of BIQA methods by integrating a host of aesthetics image features with the features of natural image statistics derived from multiple domains. The proposed features have been used for augmenting five different state-of-the-art BIQA methods, which use statistical natural scene statistics features. Experiments were performed on seven benchmark image quality databases. The experimental results showed significant improvement of the accuracy of the methods.

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Section: Review On Related Measuresmentioning

confidence: 99%

Section: Optimal Feature Selection Analysismentioning

confidence: 99%

Blind image quality assessment based on aesthetic and statistical quality-aware features

Jenadeleh

Masaeli

Moghaddam

2017

J. Electron. Imaging

View full text Add to dashboard Cite

show abstract

“…They define two objectives, where the first one maximizes the separation of clusters in the feature space, while the second one minimizes the number of selected features. Similarly, Hamdani et al use NSGA-II to minimize classification error of a nearest neighbor (1-NN) classifier as well as the number of features [9]. The same approach is used by Tekgüç et al in the context of facial expression recognition [23].…”

Section: Related Workmentioning

confidence: 99%

Feature Selection with a Budget

Richter

Maier

Gruna

et al. 2016

World Congress on Electrical Engineering and Computer Systems and Science

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-Feature selection is an important step in all practical applications of pattern recognition. As such, it is not surprising that during the past decades it has received a lot of attention from the research community. The topic is well understood and many methods have been put to the test. Most methods, however, overlook an aspect critical to real-time applications: limited computation time. The set of selected features must not only be suitable to solve the task, but must also ensure that the task can be solved within the available time. With this in mind, we propose a method for feature selection with a budget. We approach the problem by stating feature selection as a multi-objective optimization problem. This problem is solved using the well known NSGA-II algorithm. We evaluate our approach using one synthetic and two real-world datasets. We explore the properties of the genetic algorithm and investigate the classification performance of the resulting selections. Our results show that the selected feature sets are highly suitable, especially when considering real-time systems.

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“…Merelo et al {8] applied a MOEA to take into account individually the errors of type I (false positive) and type II (false negative). Hamdani et al [9] used the NSGA-II [10] algorithm to optimize simultaneously the number of features and the global error obtained by a neural network classifier.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach

Mendes

Duarte

Vieira

et al. 2010

Advances in Intelligent and Soft Computing

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Abstract. In this work a Multi-Objective Evolutionary Algorithm (MOEA) was applied for feature selection in the problem of bankruptcy prediction. The aim is to maximize the accuracy of the classifier while keeping the number of features low. A two-objective problem -minimization of the number of features and accuracy maximization -is fully analyzed using two classifiers: Support Vector Machines and Logistic Function. A database containing financial statements of 1200 medium sized private French companies was used. It was shown that MOEA is a very efficient feature selection approach. Furthermore, it can provide very useful information for the decision maker in characterizing the financial health of a company.

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Multi-objective Feature Selection with NSGA II

Cited by 125 publications

References 14 publications

Blind image quality assessment based on aesthetic and statistical quality-aware features

Blind image quality assessment based on aesthetic and statistical quality-aware features

Feature Selection with a Budget

Feature Selection for Bankruptcy Prediction: A Multi-Objective Optimization Approach

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