نتایج جستجو برای: ensemble learning techniques
تعداد نتایج: 1203533 فیلتر نتایج به سال:
Abstract here...
Customer churn is a main concern of most firms in all industries. The aim of customer churn prediction is detecting customers with high tendency to leave a company. Although, many modeling techniques have been used in the field of churn prediction, performance of ensemble methods has not been thoroughly investigated yet. Therefore, in this paper, we perform a comparative assessment of the perfo...
Ensemble methods such as AdaBoost are popular machine learning methods that create highly accurate classifier by combining the predictions from several classifiers. We present a parametrized method of AdaBoost that we call Top-k Parametrized Boost. We evaluate our and other popular ensemble methods from a classification perspective on several real datasets. Our empirical study shows that our me...
Process-based modeling is an approach to learning understandable, explanatory models of dynamic systems from domain knowledge and data. Although their utility has been proven on many tasks of modeling dynamic systems in various domains, their ability to accurately predict the future behavior of an observed system is limited. To address this limitation, we propose the use of a standard approach ...
Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend....
The nite-sample as well as the asymptotic distribution of Leung and Barrons (2006) model averaging estimator are derived in the context of a linear regression model. An impossibility result regarding the estimation of the nite-sample distribution of the model averaging estimator is obtained.
Several ensemble methods have been proposed that can accommodate differing base model types. This document reviews the recent literature, and for each method, we identify (1) main contributions, (2) theoretical motivation, (3) empirical results and (4) relationships to other techniques.
In machine learning, ensemble methods combine the predictions of multiple base learners to construct more accurate aggregate predictions. Established supervised learning algorithms inject randomness into the construction of the individual base learners in an effort to promote diversity within the resulting ensembles. An undesirable side effect of this approach is that it generally also reduces ...
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