A linear functional strategy for regularized ranking

Kriukova, Galyna; Panasiuk, Oleksandra; Pereverzyev, Sergiy; Tkachenko, Pavlo

doi:10.1016/j.neunet.2015.08.012

Cited by 28 publications

(13 citation statements)

References 14 publications

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“…The methods that we have chosen construct classifiers with different mathematical structures.Therefore each constructed classifier may capture different aspects of the ideal classifier effectively. Using a combination of classifiers constructed using different statistical learning methods may give rise to a new classifier with better accuracy (and closer to an ideal classifier) than each classifier taken individually (Chen et al 2015;Kriukova et al 2016). The design of appropriate combination strategies is another avenue that we may explore in the future.…”

Section: Discussionmentioning

confidence: 99%

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

Sreejith

Pereverzyev

Kelvin

et al. 2017

Monthly Notices of the Royal Astronomical Society

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We apply four statistical learning methods to a sample of 7941 galaxies (z < 0.06) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using 10 features measured for each galaxy (sizes, colours, shape parameters & stellar mass) we apply the techniques of Support Vector Machines (SVM), Classification Trees (CT), Classification Trees with Random Forest (CTRF) and Neural Networks (NN), returning True Prediction Ratios (TPRs) of 75.8%, 69.0%, 76.2% and 76.0% respectively. Those occasions whereby all four algorithms agree with each other yet disagree with the visual classification ('unanimous disagreement') serves as a potential indicator of human error in classification, occurring in ∼ 9% of ellipticals, ∼ 9% of Little Blue Spheroids, ∼ 14% of early-type spirals, ∼ 21% of intermediate-type spirals and ∼ 4% of late-type spirals & irregulars. We observe that the choice of parameters rather than that of algorithms is more crucial in determining classification accuracy. Due to its simplicity in formulation and implementation, we recommend the CTRF algorithm for classifying future galaxy datasets. Adopting the CTRF algorithm, the TPRs of the 5 galaxy types are : E, 70.1%; LBS, 75.6%; S0-Sa, 63.6%; Sab-Scd, 56.4% and Sd-Irr, 88.9%. Further, we train a binary classifier using this CTRF algorithm that divides galaxies into spheroid-dominated (E, LBS & S0-Sa) and disk-dominated (Sab-Scd & Sd-Irr), achieving an overall accuracy of 89.8%. This translates into an accuracy of 84.9% for spheroid-dominated systems and 92.5% for disk-dominated systems. c 0000 The Authors arXiv:1711.06125v2 [astro-ph.GA] 17 Nov 2017 2 S.Sreejith et al.

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Section: Discussionmentioning

confidence: 99%

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

Sreejith

Pereverzyev

Kelvin

et al. 2017

Monthly Notices of the Royal Astronomical Society

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“…This conclusion follows from present study (see Table 2) and previous publications. 5,6 At the same time, we understand that to bring the proposed tool closer to patients a new clinical trial entirely devoted to its testing would be desirable, and we hope that our research provides a motivation for such a trial. The design of this future trial should shed light on the following issues.…”

Section: Discussionmentioning

confidence: 97%

“…The approach is based on a recently developed strategy for aggregating ranking algorithms. 5,6 The approach has been tested on 2 different clinical datasets and exhibited a secure level of predictive accuracy outperforming previously known results.…”

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confidence: 89%

Glycemic Control Indices and Their Aggregation in the Prediction of Nocturnal Hypoglycemia From Intermittent Blood Glucose Measurements

Sampath

Tkachenko

Renard

et al. 2016

J Diabetes Sci Technol

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Background: Despite the risk associated with nocturnal hypoglycemia (NH) there are only a few methods aiming at the prediction of such events based on intermittent blood glucose monitoring data. One of the first methods that potentially can be used for NH prediction is based on the low blood glucose index (LBGI) and suggested, for example, in Accu-Chek® Connect as a hypoglycemia risk indicator. On the other hand, nowadays there are other glucose control indices (GCI), which could be used for NH prediction in the same spirit as LBGI. In the present study we propose a general approach of combining NH predictors constructed from different GCI. Methods: The approach is based on a recently developed strategy for aggregating ranking algorithms in machine learning. NH predictors have been calibrated and tested on data extracted from clinical trials, performed in EU FP7-funded project DIAdvisor. Then, to show a portability of the method we have tested it on another dataset that was received from EU Horizon 2020-funded project AMMODIT. Results: We exemplify the proposed approach by aggregating NH predictors that have been constructed based on 4 GCI associated with hypoglycemia. Even though these predictors have been preliminary optimized to exhibit better performance on the considered dataset, our aggregation approach allows a further performance improvement. On the dataset, where a portability of the proposed approach has been demonstrated, the aggregating predictor has exhibited the following performance: sensitivity 77%, specificity 83.4%, positive predictive value 80.2%, negative predictive value 80.6%, which is higher than conventionally considered as acceptable. Conclusion: The proposed approach shows potential to be used in telemedicine systems for NH prediction.

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“…In this study we consider two main approaches: Kohonen neural networks for dataset with large number of features (see [5]), and linear functional strategy (LFS) for dataset with relatively small number of features (see [6]), where use of large neural networks is inappropriate because of propensity to overfitting. LFS can be interpreted as a neural network with one hidden layer, where the basic constructed rankers play the role of activation function.…”

Section: Ann-based Classifier Modelmentioning

confidence: 99%

Artificial Neural Network for Multiclass Recognition and its Application to the Thyroid Functional State

Kriukova

Radchenko

Sudakov

2017

Research Bulletin of the National Technical University of Ukraine "Kyiv Politechnic Institute"

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Background. Development of automated diagnostic requires selection and improvement of appropriate machine learning methods, in particular multiclass recognition. Artificial Neural Networks (ANN) of various architecture are considered as an approach to the problem. Results. Performance of the classifiers is compared in terms of accuracy, sensitivity, and specificity. Linear functional strategy classifier outperforms the other with more complex ANN-architecture and exhibits relative steadiness to overfitting. Performance of Kohonen neural network on large dataset exceeds 90 % in terms of specificity for each class, withal sensitivity for distinct classes is more than 95 %. Conclusions. The understanding of the strengths and limitations of each method is crucial for careful choice of ANNbased classifier, particularly its architecture, regularization and training algorithm.

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A linear functional strategy for regularized ranking

Cited by 28 publications

References 14 publications

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

Galaxy And Mass Assembly: automatic morphological classification of galaxies using statistical learning

Glycemic Control Indices and Their Aggregation in the Prediction of Nocturnal Hypoglycemia From Intermittent Blood Glucose Measurements

Artificial Neural Network for Multiclass Recognition and its Application to the Thyroid Functional State

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