This paper develops an artificial intelligence based automated valuation model (AI-AVM) using the boosting tree ensemble technique to predict housing prices in Singapore. We use more than 300,000 private and public transactions Singapore for period from 1995 2017 training of AI-AVM models. The is best predictive that produce most robust accurate predictions compared decision multiple regression...

BACKGROUND Antiretrovirals used to treat HIV-infected patients have the potential to adversely affect serum lipid profiles and increase the risk of cardiovascular disease which is an emerging concern among HIV-infected patients. Since boosted atazanavir and efavirenz are both considered preferred antiretrovirals a head to head comparison of their effects on serum lipids is needed. AIM The pri...

Viola and Jones [1] proposed the influential rapid object detection algorithm. They used AdaBoost to select from a large pool a set of simple features and constructed a strong classifier of the form {j αjhj(x) ≥ θ} where each hj(x) is a binary weak classifier based on a simple feature. In this paper, we construct, using statistical detection theory, a binary decision tree from the strong classi...

BOOSTED REGRESSION TREES. EXCELLENT FOR DATA-POOR SPATIAL MANAGEMENT BUT HARD TO USE Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees featu...

This study utilizes a number of algorithms used in machine learning to nowcast domestic liquidity growth the Philippines. It employs regularization (i.e., Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (ENET)) tree-based Random Forest, Gradient Boosted Trees) methods order support BSP’s current suite macroeconomic models forecast analyze liquidity. Hence,...

In this paper we propose using the principle of boosting to reduce the bias of a random forest prediction in the regression setting. From the original random forest fit we extract the residuals and then fit another random forest to these residuals. We call the sum of these two random forests a one-step boosted forest. We have shown with simulated and real data that the one-step boosted forest h...

In this paper we propose a new classification algorithm designed for application on complex networks motivated by algorithmic similarities between boosting learning and message passing. We consider a network classifier as a logistic regression where the variables define the nodes and the interaction effects define the edges. From this definition we represent the problem as a factor graph of loc...

This paper makes two scientific contributions to the field of exoskeleton-based action and movement recognition. First, it presents a novel machine learning pattern recognition-based framework that can detect wide range actions movements - walking, walking upstairs, downstairs, sitting, standing, lying, stand sit, sit stand, lie, lie with an overall accuracy 82.63%. Second, comprehensive compar...

Estimating urban trees growth, especially tree height is very important in urban landscape management. The aim of the study was to predict of tree height base on tree diameter. To achieve this goal, 921 trees from five species were measured in five areas of Mashhad city in 2014. The evaluated trees were ash tree (Fraxinus species), plane tree (Platanus hybrida), white mulberry (Morus alba), ail...

The prediction of overall survival in tongue cancer is important for planning personalized care and patient counselling. This study compares the performance a nomogram with machine learning model to predict cancer. were built using large data set from Surveillance, Epidemiology, End Results (SEER) program database. comparison necessary provide clinicians comprehensive, practical, most accurate ...