Related papers: Periodicity detection in AGN with the boosted tree…

Most real-world classification problems deal with imbalanced datasets, posing a challenge for Artificial Intelligence (AI), i.e., machine learning algorithms, because the minority class, which is of extreme interest, often proves difficult…

We have carried out a multifrequency analysis of the radio variability of blazars, exploiting the data obtained during the extensive monitoring programs carried out at the University of Michigan Radio Astronomy Observatory (UMRAO, at 4.8,…

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-of-the-art results…

A large number of AGN have been monitored for nearly 30 years at 22, 37 and 87 GHz in Mets\"ahovi Radio Observatory. These data were combined with lower frequency 4.8, 8.0 and 14.5 GHz data from the University of Michigan Radio Astronomy…

We present the results of a systematic search in 8.5 years of Rossi X-ray Timing Explorer All-Sky Monitor data for evidence of periodicities. The search was conducted by application of the Lomb-Scargle periodogram to the light curves of…

This work aims to develop a computationally inexpensive approach, based on machine learning techniques, to accurately predict thousands of stellar rotation periods. The innovation in our approach is the use of the XGBoost algorithm to…

We present a study of variability time scales in a large sample of Active Galactic Nuclei at several frequencies between 4.8 and 230 GHz. We investigate the differences of various AGN types and frequencies and correlate the measured time…

Gradient boosting for decision tree algorithms are increasingly used in actuarial applications as they show superior predictive performance over traditional generalised linear models. Many enhancements to the first gradient boosting machine…

XGBoost is a scalable ensemble technique based on gradient boosting that has demonstrated to be a reliable and efficient machine learning challenge solver. This work proposes a practical analysis of how this novel technique works in terms…

XGBoost, a scalable tree boosting algorithm, has proven effective for many prediction tasks of practical interest, especially using tabular datasets. Hyperparameter tuning can further improve the predictive performance, but unlike neural…

The field of radio astronomy is witnessing a boom in the amount of data produced per day due to newly commissioned radio telescopes. One of the most crucial problems in this field is the automatic classification of extragalactic radio…

(a) Many air quality forecasting studies report gains from machine learning, but evaluations often use static chronological splits and omit persistence baselines, so the operational added value under routine updating is unclear. (b) Using…

An information theoretic approach to learning the complexity of classification and regression trees and the number of trees in gradient tree boosting is proposed. The optimism (test loss minus training loss) of the greedy leaf splitting…

Prognostication of medical problems using the clinical data by leveraging the Machine Learning techniques with stellar precision is one of the most important real world challenges at the present time. Considering the medical problem of…

We report the detection of 72 new pulses from the repeating fast radio burst FRB 121102 in Breakthrough Listen C-band (4-8 GHz) observations at the Green Bank Telescope. The new pulses were found with a convolutional neural network in data…

We present a multiband study of FRB 20180916B, a repeating source with a 16.3 day periodicity. We report the detection of 4, 1 and 7 bursts from observations spanning 3 days using upgraded Giant Metrewave Radio Telescope (300-500 MHz),…

We propose a novel tree-based ensemble method, named XGBoostPP, to nonparametrically estimate the intensity of a point process as a function of covariates. It extends the use of gradient-boosted regression trees (Chen & Guestrin, 2016) to…

We are reporting a discovery of X-ray pulsations from the source AX J1749.1-2733 with the period of ~132 s based on the XMM-Newton data obtained in March 2007. The observed pulse profile has a double-peaked structure with the pulse fraction…

Accurate short-term forecasting of air temperature and relative humidity is critical for urban management, especially in topographically complex cities such as Chongqing, China. This study compares seven machine learning models: eXtreme…

Possible periodic features in fast radio bursts (FRBs) may provide insights into their astrophysical origins. Using extensive observations from the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we conduct a multi-timescale…