Pulsar selection using fuzzy  knn  classifier

Mohamed, Taha M.

doi:10.1016/j.fcij.2017.11.001

Cited by 27 publications

(13 citation statements)

References 8 publications

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“…In the situation of HTRU 1, test dataset contained 40% candidates, while it had 20% candidates in that of HTRU 2. (The number of test data is consistent with that in Guo et al (2017) or Mohamed (2017), for comparing results. )…”

Section: Feature Relative Importancesupporting

confidence: 88%

“…The testing subset got at last section contains 20% HTRU 2 candidates, which was consistent with the operation in Mohamed (2017). The left candidates in HTRU 2 were used for both training(40%) and validation(40%).…”

Section: Evaluation With Htru 2 Datasetmentioning

confidence: 99%

“…The performance metrics of classification on HTRU 2 dataset are averaged and showed in Table 6. We also list the results of Lyon et al (2016) and Mohamed (2017) as a reference.…”

Section: Evaluation With Htru 2 Datasetmentioning

confidence: 99%

“…Besides, we found that those existing methods still returned too many false positives. For example, in HTRU2 dataset, fuzzy-knn has improved recall to be 0.942, but the precision was 0.808 (Mohamed 2017). For more complex real-world data, precision may be lower.…”

Section: Introductionmentioning

confidence: 98%

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A hybrid ensemble method for pulsar candidate classification

Wang

Pan

Zheng

et al. 2019

Astrophys Space Sci

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In this paper, three ensemble methods: Random Forest, XGBoost, and a Hybrid Ensemble method were implemented to classify imbalanced pulsar candidates. To assist these methods, tree models were used to select features among 30 features of pulsar candidates from references. The skewness of the integrated pulse profile, chi-squared value for sine-squared fit to amended profile and best S/N value play important roles in Random Forest, while the skewness of the integrated pulse profile is one of the most significant features in XGBoost. More than 20 features were selected by their relative scores and then applied in three ensemble methods. In the Hybrid Ensemble method, we combined Random Forest and XGBoost with EasyEnsemble. By changing thresholds, we tried to make a tradeoff between Recall and Precision to make them approximately equal and as high as possible. Experiments on HTRU 1 and HTRU 2 datasets show that the Hybrid Ensemble method achieves higher Recall than the other two algorithms. In HTRU 1 dataset, Recall, Precision, and F-Score of the Hybrid Ensemble method are 0.967, 0.971, and 0.969, respectively. In HTRU 2 dataset, the three values of that are 0.920, 0.917, and 0.918, respectively.

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Section: Feature Relative Importancesupporting

confidence: 88%

Section: Evaluation With Htru 2 Datasetmentioning

confidence: 99%

“…The performance metrics of classification on HTRU 2 dataset are averaged and showed in Table 6. We also list the results of Lyon et al (2016) and Mohamed (2017) as a reference.…”

Section: Evaluation With Htru 2 Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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A hybrid ensemble method for pulsar candidate classification

Wang

Pan

Zheng

et al. 2019

Astrophys Space Sci

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“…Fuzzy K-Nearest Neighbour algorithm (FKNN), presented in [8], preserves the fundamental decision rule of the KNN method and attempts to overcome both of KNN defects that mentioned in the preceding section. The comparison between FKNN and other classifiers such as crisp KNN, neural networks, Bayesian, and linear discriminant functions illustrate this method's superiority [18]. Consider X = {x 0 , x 1 , .…”

Section: Fuzzy K Nearest Neighboursmentioning

confidence: 99%

Credit Risk Assessment Using Learning Algorithms for Feature Selection

Hassani

Meybodi

Hajihashemi³

2020

Fuzzy Information and Engineering

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Firefly algorithm is one of the latest outstanding bio-inspired algorithms, which could be manipulated in solving continuous or discrete optimisation problems. In this context, we have utilised the firefly algorithm accompanied by five well-known models of feature selection classifiers to have an accurate estimation of risk, and further to improve the interpret-ability of credit card prediction. One of the significant challenges in the real-world datasets is how to select features. As most of the datasets are unbalanced, the selection of features turns to the maximum class of data that is not fair. To overcome this issue, we have balanced the data using the SMOTE method. Our experimental results on four datasets show that balancing data has increased accuracy. In addition, using a hybrid firefly algorithm, the optimal combination of features that predicts the target class label is achieved. The selected features by the proposed method besides been reduced can represent both majority and minority classes.

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Parameter Optimization of Polynomial Kernel SVM from miniCV

Yeh

2019

Machine Learning, Optimization, and Data Science

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Pulsar selection using fuzzy knn classifier

Cited by 27 publications

References 8 publications

A hybrid ensemble method for pulsar candidate classification

A hybrid ensemble method for pulsar candidate classification

Credit Risk Assessment Using Learning Algorithms for Feature Selection

Parameter Optimization of Polynomial Kernel SVM from miniCV

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