Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme

Akhtar, Faheem; Li, Jianqiang; Pei, Yan; Xu, Yang; Rajput, Asif; Wang, Qing

doi:10.1007/978-981-15-3250-4_8

Cited by 12 publications

(4 citation statements)

References 16 publications

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“…Even if data preprocessing was carried out with standardization, normalization, and et al, the classifiers, such as linear SVC, Multinomial-Naïve-Bayes and AdaBoost didn't perform better. The RFECV method worked well in other fields, such as image processing, financial data analyzing, and was already used in medical research [25,26]. The classifiers used in the study; except ExtraTrees, RandomForest and the simple deep learning model, didn't work well (with highest accuracy of 0.815) to subtype ischemic stroke (IS) with 8 neurological deficits.…”

Section: Discussionmentioning

confidence: 99%

Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Fang

Liu

2020

IEEE Access

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Ischemic stroke subtyping was not only highly valuable for effective intervention and treatment, but also important to the prognosis of ischemic stroke. The manual adjudication of disease classification was time-consuming, error-prone, and limits scaling to large datasets. In this study, an integrated machine learning approach was used to classify the subtype of ischemic stroke on The International Stroke Trial (IST) dataset. We considered the common problems of feature selection and prediction in medical datasets. Firstly, the importances of features were ranked by the Shapiro-Wilk algorithm and Pearson correlations between features were analyzed. Then, we used Recursive Feature Elimination with Cross-Validation (RFECV), which incorporated linear SVC, Random-Forest-Classifier, Extra-Trees-Classifier, AdaBoost-Classifier, and Multinomial-Naïve-Bayes-Classifier as estimator respectively, to select robust features important to ischemic stroke subtyping. Furthermore, the importances of selected features were determined by Extra-Trees-Classifier. Finally, the selected features were used by Extra-Trees-Classifier and a simple deep learning model to classify the ischemic stroke subtype on IST dataset. It was suggested that the described method could classify ischemic stroke subtype accurately. And the result showed that the machine learning approaches outperformed human professionals.

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Section: Discussionmentioning

confidence: 99%

Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Fang

Liu

2020

IEEE Access

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“…The initial input features (Table 1) are optimized by recursive feature elimination with cross-validation (Akhtar et al, 2019),…”

Section: Automatic Optimization Of Xgboost Modelmentioning

confidence: 99%

Generalized spatiotemporally-decoupled framework for reconstructing the source of non-constant atmospheric radionuclide releases

Xu,

Fang,

Dong

et al. 2023

Preprint

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Abstract. Determining the source location and release rate are critical in assessing the environmental consequences of atmospheric radionuclide releases, but remain challenging because of the huge multi-dimensional solution space. We propose a generalized spatiotemporally-decoupled two-step framework to reduce the dimension of the solution space in each step and improves the reconstruction accuracy, which is applicable to non-constant releases. The decoupling process is conducted by applying a temporal sliding-window average filter to the observations, thereby reducing the influence of temporal variations in the release rate and ensuring that the features of the filtered data are dominated by the source location. A machine learning model is trained to link these features to the source location, enabling independent source localization. Then the release rate is determined using projected alternating minimization with the L1-norm and total variation regularization algorithm. Validation using SCK-CEN 41Ar experimental data demonstrates that the localization error is less than 1 %, and the temporal variations, peak release rate, and total release are reconstructed accurately. The proposed method exhibits higher accuracy and a smaller uncertainty range than the correlation-based and Bayesian methods. Furthermore, it achieves stable performance with different hyperparameters and produces low error levels even with only a single observation site.

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“…For the combined dataset, the authors noted that ISCX2012 was only used to provide data traffic with normal activity behavior. Recursive Feature Elimination with Cross Validation [64] was performed on six learners (RF, DT, Logistic Regression, SGDClassifier [65], Adaboost, MLP). The learners were built with Scikit-learn.…”

Section: Filho Et Al [21] (Smart Detection: An Online Approach For Dmentioning

confidence: 99%

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

2020

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The exponential growth in computer networks and network applications worldwide has been matched by a surge in cyberattacks. For this reason, datasets such as CSE-CIC-IDS2018 were created to train predictive models on network-based intrusion detection. These datasets are not meant to serve as repositories for signature-based detection systems, but rather to promote research on anomaly-based detection through various machine learning approaches. CSE-CIC-IDS2018 contains about 16,000,000 instances collected over the course of ten days. It is the most recent intrusion detection dataset that is big data, publicly available, and covers a wide range of attack types. This multi-class dataset has a class imbalance, with roughly 17% of the instances comprising attack (anomalous) traffic. Our survey work contributes several key findings. We determined that the best performance scores for each study, where available, were unexpectedly high overall, which may be due to overfitting. We also found that most of the works did not address class imbalance, the effects of which can bias results in a big data study. Lastly, we discovered that information on the data cleaning of CSE-CIC-IDS2018 was inadequate across the board, a finding that may indicate problems with reproducibility of experiments. In our survey, major research gaps have also been identified.

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Optimal Features Subset Selection for Large for Gestational Age Classification Using GridSearch Based Recursive Feature Elimination with Cross-Validation Scheme

Cited by 12 publications

References 16 publications

Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Automated Ischemic Stroke Subtyping Based on Machine Learning Approach

Generalized spatiotemporally-decoupled framework for reconstructing the source of non-constant atmospheric radionuclide releases

A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data

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