Bayesian model averaging also known as the Bayes optimal classifier (BOC) is an ensemble technique used extensively in the statistics literature. However, compared to other ensemble techniques such as bagging and boosting, BOC is less known and rarely used in data mining. This is partly due to model averaging being perceived as being inefficient and because bagging and boosting consistently out...

We address the question of whether and how boosting and bagging can be used for speech recognition. In order to do this, we compare two different boosting schemes, one at the phoneme level, and one at the utterance level, with a phoneme level bagging scheme. We control for many parameters and other choices, such as the state inference scheme used. In an unbiased experiment, we clearly show that...

Wind energy is rapidly increasing and it is becoming a significant contributor to the electricity grid. Wind speed is an important factor in wind power production and integration. This paper presents a wind speed forecasting using feature selection method and bagging neural network. Feature selection plays an essential role in the machine learning environment and especially in the prediction ta...

In this paper the performance of bagging in classification problems is theoretically analysed, using a framework developed in works by Tumer and Ghosh and extended by the authors. A bias-variance decomposition is derived, which relates the expected misclassification probability attained by linearly combining classifiers trained on N bootstrap replicates of a fixed training set to that attained ...

The degree of malignancy in brain glioma needs to be assessed by MRI findings and clinical data before operations. There have been previous attempts to solve this problem by using fuzzy max-min neural networks and support vector machines (SVMs), while in this paper, a novel algorithm named PRIFEB is proposed by combining bagging of SVMs with embedded feature selection for its individuals. PRIFE...

The literature on excess return prediction has considered a wide array of estimation schemes, among them unrestricted and restricted regression coefficients. We consider bootstrap aggregation (bagging) to smooth parameter restrictions. Two types of restrictions are considered: positivity of the regression coefficient and positivity of the forecast. Bagging constrained estimators can have smalle...

Bagging of female inflorescence following pollination has recently received growers attention. Although this process occurs early in the season but affects fruit quality, yield, and marketability. Date palm growers have used to use grocery paper bags for this bagging process. Since inflorescence bagging causes what is called the greenhouse effect, it was very important to investigate the effect...

In view of pollution prediction modeling, the study adopts homogenous (random forest, bagging, and additive regression) and heterogeneous (voting) ensemble classifiers to predict the atmospheric concentration of Sulphur dioxide. For model validation, results were compared against widely known single base classifiers such as support vector machine, multilayer perceptron, linear regression and re...

Several rating agencies such as Standard & Poor's (S&P), Moody's and Fitch Ratings have evaluated firms’ credit rating. Since lots of fees are required by the agencies and sometimes the timely default risk of the firms is not reflected, it can be helpful for stakeholders if the credit ratings can be predicted before the agencies publish them. However, it is not easy to make an accurate predicti...

Bagging is one of the most effective computationally intensive procedures to improve on unstable estimators or classifiers, useful especially for high dimensional data set problems. Here we formalize the notion of instability and derive theoretical results to analyze the variance reduction effect of bagging (or variants thereof) in mainly hard decision problems, which include estimation after t...