Synthesis and characterization of metal diselenophosphates: M[Se2P(OR)2]2 (M=Pd, Pt; R=Et, nPr, iPr)

Liaw, Ben-Jie; Yu, Leeyan; Wang, Ju‐Chun; Liu, C.W.

doi:10.1016/j.poly.2008.02.021

Cited by 135 publications

(170 citation statements)

References 32 publications

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“…We define the best threshold as that which maximizes the sum of both terms (sensitivity + specificity, see the Appendix for the relevant R scripts). We also used the randomForest (Liaw & Wiener 2002) R package to fit RFs and the e1071 (Meyer et al 2014) R package to fit SVM models. Ultimately, we settled on the RF algorithm, for its overall accuracy, and LR for its slightly better sensitivity to pulsar classification.…”

Section: Classification Algorithmsmentioning

confidence: 99%

“…In this paper, we make use of the randomForest package (Liaw & Wiener 2002) in R. In order to ensure the stability of our results, we grew a large number of trees (10,000 versus the default of 500) and scanned over a range of values of m. We found that a value of m = 2 was optimum, giving an OOB 15 estimate of the error of 2.3%. 15 "Out-of-bag," in the sense that it is based on the portion of the data not already used for training the original tree, thus providing an internal estimate of the error that is expected to be comparable to that obtained with a final independent testing data set.…”

Section: Random Forestmentioning

confidence: 99%

“…Then the same is done after permuting each predictor variable. The difference between the two is then averaged over all trees (Liaw & Wiener 2002).…”

Section: Random Forestmentioning

confidence: 99%

“…This can be used to calculate the "outlyingness" of a source, as the reciprocal of the sum of squared proximities between that source and all other sources in the same class, normalized by subtracting the median and dividing by the median absolute deviation, within each class (Liaw & Wiener 2002). Figure 6 shows the "outlyingness" of all 3FGL sources with respect to the PSR and AGN classes.…”

Section: Random Forestmentioning

confidence: 99%

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Classification and Ranking of Fermi Lat Gamma-Ray Sources From the 3fgl Catalog Using Machine Learning Techniques

Parkinson

et al. 2016

ApJ

135

174

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We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope Source Catalog (3FGL), according to their likelihood of falling into the two major classes of gamma-ray emitters: pulsars (PSR) or active galactic nuclei (AGNs). Using 1904 3FGL sources that have been identified/associated with AGNs (1738) and PSR (166), we train (using 70% of our sample) and test (using 30%) our algorithms and find that the best overall accuracy (>96%) is obtained with the Random Forest (RF) technique, while using a logistic regression (LR) algorithm results in only marginally lower accuracy. We apply the same techniques on a subsample of 142 known gamma-ray pulsars to classify them into two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more, the RF algorithm has the best overall accuracy (∼90%), while a boosted LR analysis comes a close second. We apply our two best models (RF and LR) to the entire 3FGL catalog, providing predictions on the likely nature of unassociated sources, including the likely type of pulsar (YNG or MSP). We also use our predictions to shed light on the possible nature of some gamma-ray sources with known associations (e.g., binaries, supernova remnants/pulsar wind nebulae). Finally, we provide a list of plausible X-ray counterparts for some pulsar candidates, obtained using Swift, Chandra, and XMM. The results of our study will be of interest both for in-depth follow-up searches (e.g., pulsar) at various wavelengths and for broader population studies.

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Section: Classification Algorithmsmentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

“…Then the same is done after permuting each predictor variable. The difference between the two is then averaged over all trees (Liaw & Wiener 2002).…”

Section: Random Forestmentioning

confidence: 99%

Section: Random Forestmentioning

confidence: 99%

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Classification and Ranking of Fermi Lat Gamma-Ray Sources From the 3fgl Catalog Using Machine Learning Techniques

Parkinson

et al. 2016

ApJ

135

174

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“…We used the implementation of RF provided in the randomForest package (Liaw & Wiener 2002). We tuned the number of trees (ntree), the maximum number of nodes of each tree (max_nodes), and the number of features randomly chosen for each tree (mtry).…”

Section: Random Forestsmentioning

confidence: 99%

Machine learning techniques to select Be star candidates

Pérez-Ortiz

García-Varela

Quiróz

et al. 2017

A&A

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Context. Optical and infrared variability surveys produce a large number of high quality light curves. Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to train an automatic classification system. Quantities related to the magnitude density of the light curves and their Fourier coefficients have been chosen as features in previous studies. However, some of these features are not robust to the presence of outliers and the calculation of Fourier coefficients is computationally expensive for large data sets. Aims. We propose and evaluate the performance of a new robust set of features using supervised classifiers in order to look for new Be star candidates in the OGLE-IV Gaia south ecliptic pole field. Methods. We calculated the proposed set of features on six types of variable stars and also on a set of Be star candidates reported in the literature. We evaluated the performance of these features using classification trees and random forests along with the K-nearest neighbours, support vector machines, and gradient boosted trees methods. We tuned the classifiers with a 10-fold cross-validation and grid search. We then validated the performance of the best classifier on a set of OGLE-IV light curves and applied this to find new Be star candidates. Results. The random forest classifier outperformed the others. By using the random forest classifier and colours criteria we found 50 Be star candidates in the direction of the Gaia south ecliptic pole field, four of which have infrared colours that are consistent with Herbig Ae/Be stars. Conclusions. Supervised methods are very useful in order to obtain preliminary samples of variable stars extracted from large databases. As usual, the stars classified as Be stars candidates must be checked for the colours and spectroscopic characteristics expected for them.

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Development of Electronic Health Record–Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction

et al. 2021

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Key Points Question Can machine learning deployed in electronic health records be used to improve readmission risk estimation for patients following acute myocardial infarction? Findings In this cohort study examining externally validated machine learning risk models for 30-day readmission of 10 187 patients following hospitalization for acute myocardial infarction, good discrimination performance was noted at the development site, but the best discrimination did not result in the best calibration. External validation yielded significant declines in discrimination and calibration. Meaning The findings of this study highlight that robust calibration assessments are a necessary complement to discrimination when machine learning models are used to predict post–acute myocardial infarction readmission; challenges with data availability across sites, even in the presence of a common data model, limit external validation performance.

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Synthesis and characterization of metal diselenophosphates: M[Se2P(OR)2]2 (M=Pd, Pt; R=Et, nPr, iPr)

Cited by 135 publications

References 32 publications

Classification and Ranking of Fermi Lat Gamma-Ray Sources From the 3fgl Catalog Using Machine Learning Techniques

Classification and Ranking of Fermi Lat Gamma-Ray Sources From the 3fgl Catalog Using Machine Learning Techniques

Machine learning techniques to select Be star candidates

Development of Electronic Health Record–Based Prediction Models for 30-Day Readmission Risk Among Patients Hospitalized for Acute Myocardial Infarction

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