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...

AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boosting rounds. The purpose is to reduce the cumulative misclassification cost more than AdaBoost. We formally show that AdaCost reduces the upper bound of cumulative misclassification cost of the training set. Empirical...

The success of mobile robots relies on the ability to extract from the environment additional information beyond simple spatial relations. In particular, mobile robots need to have semantic information about the entities in the environment such as the type or the name of places or objects. This work addresses the problem of classifying places (room, corridor or doorway) using mobile robots equi...

We saw last time that the training error of AdaBoost decreases exponentially as the number of rounds T grows. However, this says nothing about how well the function output by AdaBoost performs on new examples. Today we will discuss the generalization error of AdaBoost. We know that AdaBoost gives us a consistent function quickly; the bound we derived on training error decreases exponentially, a...

Detecting anatomical structures, such as the carina, the pulmonary trunk and the aortic arch, is an important step in designing a CAD system of detection Pulmonary Embolism. The presented CAD system gets rid of the high-level prior defined knowledge to become a system which can easily extend to detect other anatomic structures. The system is based on a machine learning algorithm — AdaBoost and ...

Three AdaBoost variants are distinguished based on the strategies applied to update the weights for each new ensemble member. The classic AdaBoost due to Freund and Schapire only decreases the weights of the correctly classified objects and is conservative in this sense. All the weights are then updated through a normalization step. Other AdaBoost variants in the literature update all the weigh...

Adaboost is an ensemble learning algorithm that combines many other learning algorithms to improve their performance. Starting with Viola and Jones’ researches [14][15], Adaboost has often been used to local-feature selection for object detection. Adaboost by ViolaJones consists of following two optimization schemes: (1) parameter fitting of local features, and (2) selection of the best local f...

Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features, or weak classifiers, are redundant. By incorporating mutual information into AdaBoost, we propose an improved boosting algorithm in this paper. The proposed method fully examines the redundancy between candidate classifiers and selected classifiers. The classifiers thus selected ar...

AdaBoost [3] minimizes an upper error bound which is an exponential function of the margin on the training set [14]. However, the ultimate goal in applications of pattern classification is always minimum error rate. On the other hand, AdaBoost needs an effective procedure for learning weak classifiers, which by itself is difficult especially for high dimensional data. In this paper, we present ...