Abstract—Vote-boosting is a sequential ensemble learning method in which individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined as a function of the disagreement rate of the current ensemble predictions for that particular instance. Experiments using the symmetric beta distribution as th...

Excellent ranking power along with well calibrated probability estimates are needed in many classification tasks. In this paper, we introduce a technique, Calibrated Boosting-Forest1 that captures both. This novel technique is an ensemble of gradient boosting machines that can support both continuous and binary labels. While offering superior ranking power over any individual regression or clas...

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...

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...

We present the IUCL system, based on supervised learning, for the shared task on stance detection. Our official submission, the random forest model, reaches a score of 63.60, and is ranked 6th out of 19 teams. We also use gradient boosting decision trees and SVM and merge all classifiers into an ensemble method. Our analysis shows that random forest is good at retrieving minority classes and gr...

Importance Sampled Circuit Learning Ensembles (ISCLEs) is a novel analog circuit topology synthesis method that returns designertrustworthy circuits yet can apply to a broad range of circuit design problems including novel functionality. ISCLEs uses the machine learning technique of boosting, which does importance sampling of “weak learners” to create an overall circuit ensemble. In ISCLEs, the...

Ensemble methods are known to increase the performance of learning algorithms, both on supervised and unsupervised learning. Boosting algorithms are quite successful in supervised ensemble methods. These algorithms build incrementally an ensemble of classifiers by focusing on objects previously misclassified while training the current classifier. In this paper we propose an extension to the Evi...

One of the potential advantages of multiple classifier systems is an increased robustness to noise and other imperfections in data. Previous experiments on classification noise have shown that bagging is fairly robust but that boosting is quite sensitive. Decorate is a recently introduced ensemble method that constructs diverse committees using artificial data. It has been shown to generally ou...

The functional classification of genes plays a vital role in molecular biology. Detecting previously unknown role of genes and their products in physiological and pathological processes is an important and challenging problem. In this work, information from several biological sources such as comparative genome sequences, gene expression and protein interactions are combined to obtain robust res...