Related papers: Studies of Boosted Decision Trees for MiniBooNE Pa…
A detailed study of the particle identification by the Focusing Aerogel Ring Imaging CHerenkov subsystem at the future charm superfactory detector is presented. The dedicated signal ring reconstruction algorithm is implemented in the…
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…
The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives. As a consequence, research into interpretable machine learning has rapidly grown in an attempt to better control and fix…
The MiniBooNE (Booster Neutrino Experiment) experiment is a precise search for neutrino oscillations but is crucially sensitive to background processes that would mimic an oscillation signal. The background sources include intrinsic…
The use of neural networks for signal vs.~background discrimination in high-energy physics experiment has been investigated and has compared favorably with the efficiency of traditional kinematic cuts. Recent work in top quark…
There are many occasions when one does not have complete information in order to classify objects into different classes, and yet it is important to do the best one can since other decisions depend on that. In astronomy, especially…
MicroBooNE is a neutrino experiment that utilizes a liquid argon time projection chamber (LArTPC) located on-axis in the Booster Neutrino Beam (BNB) at Fermilab. One of the experiment's main goals is to search for excess low-energy…
In order to speed-up classification models when facing a large number of categories, one usual approach consists in organizing the categories in a particular structure, this structure being then used as a way to speed-up the prediction…
In recent years, gradient boosted decision trees have become popular in building robust machine learning models on big data. The primary technique that has enabled these algorithms success has been distributing the computation while…
The MiniBooNE experiment at Fermilab reports results from an analysis of $\bar \nu_e$ appearance data from $11.27 \times 10^{20}$ protons on target in antineutrino mode, an increase of approximately a factor of two over the previously…
The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced,…
The MicroBooNE detector has recently completed its first year of neutrino beam data-taking in the Booster Neutrino Beam at Fermilab, having collected approximately half of its intended data ($3.4\times10^{20}$ of $6.6\times10^{20}$ protons…
Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles like…
This work explores the use of gradient boosting in the context of classification. Four popular implementations, including original GBM algorithm and selected state-of-the-art gradient boosting frameworks (i.e. XGBoost, LightGBM and…
Classifier chains is a key technique in multi-label classification, since it allows to consider label dependencies effectively. However, the classifiers are aligned according to a static order of the labels. In the concept of dynamic…
MiniBooNE, the Mini Booster Neutrino Experiment at Fermilab, will search for the appearance of electron neutrinos in a beam of muon neutrinos, and in the process will accumulate more neutrino interactions in the 0-2 GeV energy range than…
Long and short baseline neutrino oscillation experiments, such as DUNE, ANNIE, SBND, demand high precision in reducing systematic errors, particularly those related to neutrino-nucleus interaction cross-sections. The stroboscopic approach…
Decision tree learning is a widely used approach in machine learning, favoured in applications that require concise and interpretable models. Heuristic methods are traditionally used to quickly produce models with reasonably high accuracy.…
Gradient Boosted Decision Trees (GBDT) is a very successful ensemble learning algorithm widely used across a variety of applications. Recently, several variants of GBDT training algorithms and implementations have been designed and heavily…
The MicroBooNE neutrino experiment at Fermilab is constructing a liquid-argon time-projection chamber for the Booster Neutrino Beam to study neutrino oscillations and interactions with nucleons and nuclei, starting in 2014. We describe the…