Boosting is an ensemble based method which attempts to boost the accuracy of any given learning algorithm by applying it several times on slightly modi ed training data and then combining the results in a suitable manner. The boosting algorithms that we covered in class were AdaBoost, LPBoost, TotalBoost, SoftBoost, and Entropy Regularized LPBoost. The basic idea behind these boosting algorithm...

Human identification at a distance has recently become a hot research topic in the fields of computer vision and pattern recognition. Gait recognition has most widely been studied to address this problem, because gait patterns can operate from a distance without subject cooperation. In this paper, a local patch-based subspace ensemble learning method for gait recognition is proposed. This metho...

Classification is a data mining task that allocated similar data to categories or classes. One of the most general methods for classification is ensemble method which refers supervised learning. After generating classification rules we can apply those rules on unidentified data and achieve the results. In oneclass classification it is supposed that only information of one of the classes, the ta...

Ensemble methods for supervised machine learning have become popular due to their ability to accurately predict class labels with groups of simple, lightweight “base learners.” While ensembles offer computationally efficient models that have good predictive capability, they tend to be large and offer little insight into the patterns or structure in a dataset. In this study, we extend an ensembl...

Boosting has established itself as a successful technique for decreasing the generalization error of classification learners by basing predictions on ensembles of hypotheses. While previous research has shown that this technique can be made to work efficiently even in the context of multirelational learning by using simple learners and active feature selection, such approaches have relied on si...

This research presents a new learning model, the Parallel Decision DAG (PDDAG), and shows how to use it to represent an ensemble of decision trees while using significantly less storage. Ensembles such as Bagging and Boosting have a high probability of encoding redundant data structures, and PDDAGs provide a way to remove this redundancy in decision tree based ensembles. When trained by encodin...

Decorrelated and CELS are two ensembles that modify the learning procedure to increase the diversity among the networks of the ensemble. Although they provide good performance according to previous comparatives, they are not as well known as other alternatives, such as Bagging and Boosting, which modify the learning set in order to obtain classifiers with high performance. In this paper, two di...

This paper proposes a boosting-like method to train a classifier ensemble from data streams. It naturally adapts to concept drift and allows to quantify the drift in terms of its base learners. The algorithm is empirically shown to outperform learning algorithms that ignore concept drift. It performs no worse than advanced adaptive time window and example selection strategies that store all the...

Machine Learning tools are increasingly being applied to analyze data from microarray experiments. These include ensemble methods where weighted votes of constructed base classifiers are used to classify data. We compare the performance of AdaBoost, bagging and BagBoost on gene expression data from the yeast cell cycle. AdaBoost was found to be more effective for the data than bagging. BagBoost...