Mixed Feature Selection Method Based on SVM

Lei, Hairui; Xiu-feng, Gao

doi:10.1007/978-3-319-98776-7_133

Cited by 2 publications

(3 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, based on the feature relevance ranking results, the features in each class with high feature-category relevance are selected for retention, the remaining features in the same class are considered redundant features for removal and the top N 2 features are retained to obtain the optimal feature subset S 2 . Studies have shown [25] that the proportion of features retained in the final feature selection should preferably be between 20% and 40%. Therefore, the number of features N 2 = Total number of datasets * 40.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

“…This experiment was set up as follows, setting the labelling rate R of the experimental dataset to 0.3, that is, 30% of the dataset was randomly selected as the training set of labelled samples, and the remaining 70% of unlabelled samples were used as the auxiliary dataset and test set. With reference to the literature [25], the number of features selected was set to 40% of the original number of features. In order to eliminate chance from the experimental results, the experiment was repeated 20 times for a specific tagging rate R for the method in this paper and the chosen comparison method, and the average of the 20 times was taken as the final result of the experiment.…”

Section: Experiments and Analysismentioning

confidence: 99%

See 1 more Smart Citation

An Integrated Semi-supervised Software Defect Prediction Model

Fanqi Meng,

Wenying Cheng

2023

Journal of Internet Technology

View full text Add to dashboard Cite

<p>A novel semi-supervised software defect prediction model FFeSSTri (Filtered Feature Selecting, Sample and Tri-training) is proposed to address the problem that class imbalance and too many irrelevant or redundant features in labelled samples lower the accuracy of semi-supervised software defect prediction. Its innovation lies in that the construction of FFeSSTri integrates an oversampling technique, a new feature selection method, and a Tri-training algorithm, thus it can effectively improve the accuracy. Firstly, the oversampling technique is applied to expand the class of inadequate samples, thus it solves the unbalanced classification of the labelled samples. Secondly, a new filtered feature selection method based on relevance and redundancy is proposed, which can exclude those irrelevant or redundant features from labelled samples. Finally, the Tri-training algorithm is used to learn the labelled training samples to build the defect prediction model FFeSSTri. The experiments conducted on the NASA software defect prediction dataset show that FFeSSTri outperforms the existing four supervised learning methods and one semi-supervised learning method in terms of F-Measure values and AUC values.</p> <p> </p>

show abstract

Section: Algorithm Descriptionmentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

An Integrated Semi-supervised Software Defect Prediction Model

Fanqi Meng,

Wenying Cheng

2023

Journal of Internet Technology

View full text Add to dashboard Cite

show abstract

“…The investigations have revealed that inclusion of the trivial features while training the models not only increases the computational complexity of the algorithm but also adversely impacts the prediction accuracy of the model [ 28 , 29 , 30 ]. It is worth noting that usually the machine learning tools perform efficiently under the circumstances where the decision boundaries are well-defined.…”

Section: Introductionmentioning

confidence: 99%

Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine

et al. 2022

View full text Add to dashboard Cite

The recent elevation of cases infected from novel COVID-19 has placed the human life in trepidation mode, especially for those suffering from comorbidities. Most of the studies in the last few months have undeniably raised concerns for hypertensive patients that face greater risk of fatality from COVID-19. Furthermore, one of the recent WHO reports has estimated a total of 1.13 billion people are at a risk of hypertension of which two-thirds live in low and middle income countries. The gradual escalation of the hypertension problem andthe sudden rise of COVID-19 cases have placed an increasingly higher number of human lives at risk in low and middle income countries. To lower the risk of hypertension, most physicians recommend drugs that have angiotensin-converting enzyme (ACE) inhibitors. However, prolonged use of such drugs is not recommended due to metabolic risks and the increase in the expression of ACE-II which could facilitate COVID-19 infection. In contrast, the intake of optimal macronutrients is one of the possible alternatives to naturally control hypertension. In the present study, a nontrivial feature selection and machine learning algorithm is adopted to intelligently predict the food-derived antihypertensive peptide. The proposed idea of the paper lies in reducing the computational power while retaining the performance of the support vector machine (SVM) by estimating the dominant pattern in the features space through feature filtering. The proposed feature filtering algorithm has reported a trade-off performance by reducing the chances of Type I error, which is desirable when recommending a dietary food to patients suffering from hypertension. The maximum achievable accuracy of the best performing SVM models through feature selection are 86.17% and 85.61%, respectively.

show abstract

Mixed Feature Selection Method Based on SVM

Cited by 2 publications

References 7 publications

An Integrated Semi-supervised Software Defect Prediction Model

An Integrated Semi-supervised Software Defect Prediction Model

Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine

Contact Info

Product

Resources

About