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...

Bagging forms a committee of classijiers by bootstrap aggregation of training sets from a pool of training data. A simple alternative to bagging is to partition the data into disjoint subsets. Experiments on various datasets show that, given the same size partitions and bags, disjoint partitions result in betterperformance than bootstrap aggregates (bags). Many applications (e.g., protein struc...

The incidence of Dupuytren's contracture in a polyvinyl chloride (PVC) manufacturing plant, where a great deal of bagging and packing took place by hand, was higher than in another plant in which there was no bagging or packing. The incidence in the packing plant was double that found in an earlier survey by Early at Crewe Locomotive Works of 4801 individuals, most of whom were manual workers. ...

Aim at solving the existing problems of 3D model retrieval based on neural network, this paper proposes a new algorithm based on BP-bagging. Through bagging, the algorithm turns the weak classifier into the strong. As to feature extraction, the algorithm projections 3D model into six 2D images by six perspective points. Then transforms the images into frequency domain, gets the high dimension f...