Financial-market risk, commonly measured in terms of asset-return volatility, plays a fundamental role in investment decisions, risk management and regulation. In this paper, we investigate a new modeling strategy that helps to better understand the forces that drive market risk. We use componentwise gradient boosting techniques to identify financial and macroeconomic factors influencing volati...

Alternating Decision Tree (ADTree) is a successful classification model based on boosting and has a wide range of applications. The existing ADTree induction algorithms apply a “top-down” strategy to evaluate the best split at each boosting iteration, which is very time-consuming and thus is unsuitable for modeling on large data sets. This paper proposes a fast ADTree induction algorithm (BOAI)...

a r t i c l e i n f o The idea of boosting deeply roots in our daily life practice, which constructs the general aspects of how to think about chemical problems and how to build chemical models. In mathematics, boosting is an iterative reweighting procedure by sequentially applying a base learner to reweighted versions of the training data whose current weights are modified based on how accurat...

In simulation studies boosting algorithms seem to be susceptible to noise. This article applies Ada.Boost.M2 used with decision stumps to the digit recognition example, a simulated data set with attribute noise. Although the final model is both simple and complex enough, boosting fails to reach the Bayes error. A detailed analysis shows some characteristics of the boosting trials which influenc...

We revisit the connection between boosting algorithms and hard-core set constructions discovered by Klivans and Servedio. We present a boosting algorithm with a certain smoothness property that is necessary for hard-core set constructions: the distributions it generates do not put too much weight on any single example. We then use this boosting algorithm to show the existence of hard-core sets ...

This article has been compiled to identify the best model of housing price forecasting using machine learning methods with maximum accuracy and minimum error. Five important machine learning algorithms are used to predict housing prices, including Nearest Neighbor Regression Algorithm (KNNR), Support Vector Regression Algorithm (SVR), Random Forest Regression Algorithm (RFR), Extreme Gradient B...

Gradient Boosting and bagging applied to regressors can reduce the error due to bias and variance respectively. Alternatively, Stochastic Gradient Boosting (SGB) and Iterated Bagging (IB) attempt to simultaneously reduce the contribution of both bias and variance to error. We provide an extensive empirical analysis of these methods, along with two alternate bias-variance reduction approaches — ...