As an artificial intelligence method, machine learning (ML) has been widely used in prediction models of high-dimensional datasets. This study proposes ML the Gradient Boosted Regression Tree (GBRT), to predict intensity changes tropical cyclones (TCs) Western North Pacific at 12-, 24-, 36-, 48-, 60-, and 72-h (hr) forecasting lead time model is optimized by Bayesian Optimization algorithm. The...

1. Ecologists use statistical models for both explanation and prediction, and need techniques that are flexible enough to express typical features of their data, such as nonlinearities and interactions. 2. This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fi...

INTRODUCTION The prediction of plasma protein binding (ppb) is of paramount importance in the pharmacokinetics characterization of drugs, as it causes significant changes in volume of distribution, clearance and drug half life. This study utilized Quantitative Structure - Activity Relationships (QSAR) for the prediction of plasma protein binding. METHODS Protein binding values for 794 compoun...

We present the system developed by the team DUTH for the participation in Semeval-2017 task 5 Fine-Grained Sentiment Analysis on Financial Microblogs and News, in subtasks A and B. Our approach to determine the sentiment of Microblog Messages and News Statements & Headlines is based on linguistic preprocessing, feature engineering, and supervised machine learning techniques. To train our model,...

Model distillation was originally designed to distill knowledge from a large, complex teacher model to a faster, simpler student model without significant loss in prediction accuracy. We investigate model distillation for another goal – transparency – investigating if fully-connected neural networks can be distilled into models that are transparent or interpretable in some sense. Our teacher mo...

Abstract Varying coefficient models are a flexible extension of generic parametric whose coefficients functions set effect-modifying covariates instead fitted constants. They capable achieving higher model complexity while preserving the structure underlying models, hence generating interpretable predictions. In this paper we study use gradient boosted decision trees as those coefficient-decidi...

With this research, we sought to examine the performance of six different regression tree data mining methods to predict mortality in head injury. Using a data set consisting of 1603 head injury cases, we assessed the performance of: the Classification and Regression Trees (CART) method; the Chi-squared Automatic Interaction Detector (CHAID) method; the Exhaustive CHAID (E-CHAID) method; the Qu...

We modeled summer and winter habitat suitability of Marco Polo argali in the Pamir Mountains in southeastern Tajikistan using these statistical algorithms: Generalized Linear Model, Random Forest, Boosted Regression Tree, Maxent, and Multivariate Adaptive Regression Splines. Using sheep occurrence data collected from 2009 to 2015 and a set of selected habitat predictors, we produced summer and ...

We present an efficient method of fitting a set of local feature models to an image within the popular Active Shape Model (ASM) framework [3]. We compare two different types of non-linear boosted feature models trained using GentleBoost [9]. The first type is a conventional feature detector classifier, which learns a discrimination function between the appearance of a feature and the local neig...