Joint sample and feature selection via sparse primal and dual LSSVM

Shao, Yuan‐Hai; Li, Chun‐Na; Huang, Ling-Wei; Wang, Zhen; Deng, Ning; Xu, Yan

doi:10.1016/j.knosys.2019.104915

Cited by 16 publications

(3 citation statements)

References 36 publications

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“…In addition, CNN usually uses Softmax for classification, but experiments have shown that Softmax is not suitable in the field of FER due to the low distinction between expressions [25,26]. Currently, many researchers combine the features extracted by CNN with traditional classifiers to have better performance and achieve good results [27][28][29][30]. Liu [31] proposed a multilevel structured hybrid forest (MSHF) for joint head detection and pose estimation, which extends the hybrid framework of classification and regression forest.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

Meng

Yuan

et al. 2021

Mathematical Problems in Engineering

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In allusion to the shortcomings of traditional facial expression recognition (FER) that only uses a single feature and the recognition rate is not high, a FER method based on fusion of transformed multilevel features and improved weighted voting SVM (FTMS) is proposed. The algorithm combines the transformed traditional shallow features and convolutional neural network (CNN) deep semantic features and uses an improved weighted voting method to make a comprehensive decision on the results of the four trained SVM classifiers to obtain the final recognition result. The shallow features include local Gabor features, LBP features, and joint geometric features designed in this study, which are composed of distance and deformation characteristics. The deep feature of CNN is the multilayer feature fusion of CNN proposed in this study. This study also proposes to use a better performance SVM classifier with CNN to replace Softmax since the poor distinction between facial expressions. Experiments on the FERPlus database show that the recognition rate of this method is 17.2% higher than that of the traditional CNN, which proves the effectiveness of the fusion of the multilayer convolutional layer features and SVM. FTMS-based facial expression recognition experiments are carried out on the JAFFE and CK+ datasets. Experimental results show that, compared with the single feature, the proposed algorithm has higher recognition rate and robustness and makes full use of the advantages and characteristics of different features.

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Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

Meng

Yuan

et al. 2021

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…It is often difficult to select the optimal parameter value, and the accuracy of fitting model is difficult to reach the expected value. Finding the right method to extract the best parameter values can improve the accuracy of the fitted model (Lin, D., 2019;Shao, Y. H., 2019). Compared with other intelligent algorithms, artificial bee colony (ABC) has the characteristics of less parameters, simple calculation and global search for optimal values (Hajimirzaei, B.,2019).…”

Section: Introductionmentioning

confidence: 99%

Research on GPS Height Fitting Based on Abc Optimized LSSVM

Zhang¹,

Huang²,

Tang³

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Aiming at the problem that the fitting parameters of the least squares support vector machine fitting method are difficult to select, a method of introducing the artificial bee colony algorithm into the least squares support vector machine to establish a high-precision region fitting model is proposed. The artificial bee colony algorithm can perform global tracking search on the parameters in the least squares support vector machine, imitate the honey collecting process of the bees, and use the primary value of the parameters as the honey source, and the average square error predicted by the least squares support vector machine as the target. The function value is determined by iterative update within a certain range to determine the optimal parameters, and finally a GPS height fitting model with higher precision is established. Experimental analysis, compared with the conventional least squares support vector machine fitting method, the accuracy of the fitting model constructed by the ABC-LSSVM combination method is improved by 45.4%. At the same time, the combined method is better than the particle swarm optimization fitting method and BP neural network. The legal convergence effect is higher and the stability is better. The effective feasibility of the ABC-LSSVM combination method in the construction of GPS height fitting model is proved, which provides a certain reference value for the establishment of GPS height fitting model. This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-3-W10-1307-2020 | © Authors 2020. CC BY 4.0 License.

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“…Based on ACO, Akinyelu et al [13] proposed an instance selection algorithm for SVM speed optimization. Shao et al [14] combined instance and feature selection, and proposed an uniform sparse primal and dual LSSVM model. Furthermore, Du et al [15] found that if the feature and instance selection are addressed separately, the irrelevant features may mislead the process of instance selection.…”

mentioning

confidence: 99%

Optimal instance subset selection from big data using genetic algorithm and open source framework

Zhai

Song

2022

J Big Data

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Data is accumulating at an incredible rate, and the era of big data has arrived. Big data brings great challenges to traditional machine learning algorithms, it is difficult for learning tasks in big data scenario to be completed on stand-alone. Data reduction is an effective way to solve this problem. Data reduction includes attribute reduction and instance reduction. In this study, we focus on instance reduction also called instance selection, and view the instance selection as an optimal instance subset selection problem. Inspired by the ideas of cross validation and divide and conquer, we defined a novel criterion called combined information entropy with respect to a set of classifiers to measure the importance of an instance subset, the criterion uses multiple independent classifiers trained on different subsets to measure the optimality of an instance subset. Based on the criterion, we proposed an approach which uses genetic algorithm and open source framework to select optimal instance subset from big data. The proposed algorithm is implemented on two open source big data platforms Hadoop and Spark, the conducted experiments on four artificial data sets demonstrate the feasibility of the proposed algorithm and visualize the distribution of selected instances, and the conducted experiments on four real data sets compared with three closely related methods on test accuracy and compression ratio demonstrate the effectiveness of the proposed algorithm. Furthermore, the two implementations on Hadoop and Spark are also experimentally compared. The experimental results show that the proposed algorithm provides excellent performance and outperforms the three methods.

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Joint sample and feature selection via sparse primal and dual LSSVM

Cited by 16 publications

References 36 publications

Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

Research on GPS Height Fitting Based on Abc Optimized LSSVM

Optimal instance subset selection from big data using genetic algorithm and open source framework

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