Improving the classification accuracy of the weld defect by chaos-search-based feature selection

Shen, Qingming; Gao, Jianmin

doi:10.1784/insi.2010.52.10.530

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Chen et al [30] combined chaos optimization with a GA to choose a subset of available features by eliminating unnecessary features from the classification task. Shen and Gao [31] proposed feature selection based on a chaos search to improve the classification accuracy of the weld detect.…”

Section: Chaos Optimization and Geneticmentioning

confidence: 99%

An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

Bian

Peng

Wang

et al. 2016

Mathematical Problems in Engineering

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In the era of big data, feature selection is an essential process in machine learning. Although the class imbalance problem has recently attracted a great deal of attention, little effort has been undertaken to develop feature selection techniques. In addition, most applications involving feature selection focus on classification accuracy but not cost, although costs are important. To cope with imbalance problems, we developed a cost-sensitive feature selection algorithm that adds the cost-based evaluation function of a filter feature selection using a chaos genetic algorithm, referred to as CSFSG. The evaluation function considers both feature-acquiring costs (test costs) and misclassification costs in the field of network security, thereby weakening the influence of many instances from the majority of classes in large-scale datasets. The CSFSG algorithm reduces the total cost of feature selection and trades off both factors. The behavior of the CSFSG algorithm is tested on a large-scale dataset of network security, using two kinds of classifiers: C4.5 andk-nearest neighbor (KNN). The results of the experimental research show that the approach is efficient and able to effectively improve classification accuracy and to decrease classification time. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

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Section: Chaos Optimization and Geneticmentioning

confidence: 99%

An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

Bian

Peng

Wang

et al. 2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…(4) Wang and Liao extracted 12 characteristics of weld defects, and classified weld defects into different types by multi-layer perceptron (MLP) neural network (5) . Shen et al defined 8 defect features and proposed an automatic classification system based on the SVM method (8,9) .…”

Section: Introductionmentioning

confidence: 99%

A novel classification approach of weld defects based on dual-parameters optimization of PCA and LDA

Mu¹,

Liu²,

Liu³

et al. 2015

Proceedings of the 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics

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To improve the classification accuracy of defects, a novel algorithm has been developed based on dual-parameters optimization of the principal component analysis (PCA) and the linear discriminant analysis (LDA). The original defect images are transformed to eigen-defects by PCA which contains all features of these defects. Then, LDA is used to classify eigen-defects. The optimal parameters of PCA and LDA are given when the PCA-LDA model gets the maximum value of classification accuracy. For estimating the actual classification accuracy of the proposed method in a concrete system, Bootstrap evaluation method is introduced. The experimental result demonstrates that the accuracy of this method is 91.12%, which promotes the accuracy by 0.37%, 3.61% and 8.72% comparing with PCA-SVM, SVM and MLP-ANN.

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Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory

et al. 2017

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Improving the classification accuracy of the weld defect by chaos-search-based feature selection

Cited by 4 publications

References 19 publications

An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

An Efficient Cost-Sensitive Feature Selection Using Chaos Genetic Algorithm for Class Imbalance Problem

A novel classification approach of weld defects based on dual-parameters optimization of PCA and LDA

Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory

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