Adolfo J. Quiroz

Herein, theoretical results are presented to provide insights into the effectiveness of subsampling methods in reducing the amount of instances required in the training stage when applying support vector machines (SVMs) for classification in big data scenarios. Our main theorem states that under some conditions, there exists, with high probability, a feasible solution to the SVM problem for a randomly chosen training subsample, with the corresponding classifier as close as desired (in terms of classification error) to the classifier obtained from training with the complete dataset. The main theorem also reflects the curse of dimensionalityin that the assumptions made for the results are much more restrictive in large dimensions; thus, subsampling methods will perform better in lower dimensions. Additionally, we propose an importance sampling and bagging subsampling method that expands the nearest-neighbors ideas presented in previous work. Using different benchmark examples, the method proposed herein presents a faster solution to the SVM problem (without significant loss in accuracy) compared with the available state-of-the-art techniques.

High quality risk adjustment in health insurance markets weakens insurer incentives to engage in inefficient behavior to attract lower-cost enrollees. We propose a novel methodology based on Markov Chain Monte Carlo methods to improve risk adjustment by clustering diagnostic codes into risk groups optimal for health expenditure prediction. We test the performance of our methodology against common alternatives using panel data from 500 thousand enrollees of the Colombian Healthcare System. Results show that our methodology outperforms common alternatives and suggest that it has potential to improve access to quality healthcare for the chronically ill.

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.

EPJ Web of Conferences, open-access proceedings in physics and astronomy

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. 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. We calculated the proposed set of features on six types of variable stars and 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 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 validated the performance of the best classifier on a set of OGLE-IV light curves and applied this to find new Be star candidates. The random forest classifier outperformed the others. By using the random forest classifier and colour criteria we found 50 Be star candidates in the direction of the Gaia south ecliptic pole field, four of which have infrared colours consistent with Herbig Ae/Be stars. 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.