A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier

Allias, Noormadinah; Noor, Megat Norulazmi Megat Mohamed; Ismail, Mohd Nazri; Silva, Kim de

doi:10.1109/aims.2013.24

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…After the above analysis, the IDA algorithm can definitely improve the speed of convergence to the optimal solution. [20] 0.227 Grid search method [21] 0.203 GA-SVR [21] 0.0168 FA-SVR [22] 0.0149 PSO-SVR [23] 0.0117 IDA-SVR 0.0092…”

Section: Comparative Experimentmentioning

confidence: 99%

“…2 Complexity the search space of the feature subset grows exponentially. Most traditional feature selection algorithms are of low efficiency, so many scholars turn to using intelligent algorithms with stronger search ability, such as genetic algorithm [19], particle swarm optimization [20], and so on. However, to obtain satisfactory prediction accuracy, it is not only related to the input characteristics of SVR but also closely related to the selection of SVR model parameters.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Students’ Performance Based on the Hybrid IDA‐SVR Model

2022

Complexity

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Students’ performance is an important factor for the evaluation of teaching quality in colleges. The aim of this study is to propose a novel intelligent approach to predict students’ performance using support vector regression (SVR) optimized by an improved duel algorithm (IDA). To the best of our knowledge, few research studies have been developed to predict students’ performance based on student behavior, and the novelty of this study is to develop a new hybrid intelligent approach in this field. According to the obtained results, the IDA-SVR model clearly outperformed the other models by achieving less mean square error (MSE). In other words, IDA-SVR with an MSE of 0.0089 has higher performance than DT with an MSE of 0.0326, SVR with an MSE of 0.0251, ANN with an MSE of 0.0241, and PSO-SVR with an MSE of 0.0117. To investigate the efficacy of IDA, other parameter optimization methods, that is, the direct determination method, grid search method, GA, FA, and PSO, are used for a comparative study. The results show that the IDA algorithm can effectively avoid the local optima and the blindness search and can definitely improve the speed of convergence to the optimal solution.

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Section: Comparative Experimentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Students’ Performance Based on the Hybrid IDA‐SVR Model

2022

Complexity

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“…Li et al [44] proposed an effective feature selection method for hyperspectral image classification based on GA. Huang and Dun [32] combined the discrete particle swarm optimization (PSO) with the continuous-valued PSO to select the input feature subset. Allias et al [7] studied the performance of PSO for feature selection on different classifiers and different population sizes. Miguel et al [28] developed a hybrid metaheuristic based on variable neighborhood search (VNS) and tabu search (TS) for feature selection in classification.…”

Section: Figure 1 Stages Of Semiconductor Manufacturingmentioning

confidence: 99%

A novel quality prediction method based on feature selection considering high dimensional product quality data

Hu¹,

Qian²,

Pei³

et al. 2022

JIMO

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Product quality is the lifeline of enterprise survival and development. With the rapid development of information technology, the semiconductor manufacturing process produces multitude of quality features. Due to the increasing quality features, the requirement on the training time and classification accuracy of quality prediction methods becomes increasingly higher. Aiming at realizing the quality prediction for semiconductor manufacturing process, this paper proposes a modified support vector machine (SVM) model based on feature selection, considering the high dimensional and nonlinear characteristics of data. The model first improves the Radial Basis Function (RBF) in SVM, and then combines the Duelist algorithm (DA) and variable neighborhood search algorithm (VNS) for feature selection and parameters optimization. Compared with some other SVM models that are based on DA, genetic algorithm (GA), and Information Gain algorithm (IG), the experiment results show that our DA-VNS-SVM can obtain higher classification accuracy rate with a smaller feature subset. In addition, we compare the DA-VNS-SVM with some common machine learning algorithms such as logistic regression, naive Bayes, decision tree, random forest, and artificial neural network. The results indicate that our model outperform these machine learning algorithms for the quality prediction of semiconductor.

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“…The article [8] presents a comparative analysis of the particle swarm method and the bee algorithm as the solution of the protein structure prediction problem. A new method of PSOVina (Particle Swarm Optimization Vina), which combines PSO with the effective local search method of Broyden-Fletcher-GoldfarbShannon (BFGS) is proposed in the article [9].…”

mentioning

confidence: 99%

Computer modeling of new drugs based on the methods of swarm intelligence and immune network modeling

Samigulina¹,

Massimkanova²

2017

Bulletin of the National Technical University "KhPI". A Series of "Information and Modeling"

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The article is devoted to the development of an intellectual information system for conducting scientific research and for predicting the dependence "structure-property/activity" of new drugs based on algorithms of swarm intelligence and immune-network modeling. Selection of informative descriptors is carried out on the basis of a multi-algorithmic approach, which allows selecting the algorithm in which the generalization error will be minimal after immune network modeling. Algorithms are distinguished by the presence of customizable parameters that affect the productivity of an intelligent information system. Figs

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A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier

Cited by 6 publications

References 8 publications

Prediction of Students’ Performance Based on the Hybrid IDA‐SVR Model

Prediction of Students’ Performance Based on the Hybrid IDA‐SVR Model

A novel quality prediction method based on feature selection considering high dimensional product quality data

Computer modeling of new drugs based on the methods of swarm intelligence and immune network modeling

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