background non-alcoholic fatty liver disease (nafld) is the most common form of liver disease in many parts of the world. objectives the aim of the present study was to identify the most important factors influencing nafld using a classification tree (ct) to predict the probability of nafld. patients and methods this cross-sectional study was conducted in kavar, a town in the south of fars prov...

Ensembles of decision trees often exhibit greater predictive accuracy than single trees alone. Bagging and boosting are two standard ways of generating and combining multiple trees. Boosting has been empirically determined to be the more eeective of the two, and it has recently been proposed that this may be because it produces more diverse trees than bagging. This paper reports empirical nding...

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...

Grafted trees are trees that are constructed using two methods. The first method creates an initial tree, while the second method is used to complete the tree. In this work, the first classifier is an unpruned tree from a 10% sample of the training data. Grafting is a method for constructing ensembles of decision trees, where each tree is a grafted tree. Grafting by itself is better than Baggin...

Ensemble methods with Random Oracles have been proposed recently (Kuncheva and Rodŕıguez, 2007). A random-oracle classifier consists of a pair of classifiers and a fixed, randomly created oracle that selects between them. Ensembles of random-oracle decision trees were shown to fare better than standard ensembles. In that study, the oracle for a given tree was a random hyperplane at the root of ...

We investigate machine learning techniques for coping with highly skewed class distributions in two spontaneous speech processing tasks. Both tasks, sentence boundary and disfluency detection, provide important structural information for downstream language processing modules. We examine the effect of data set size, task, sampling method (no sampling, downsampling, oversampling, and ensemble sa...

In this study, we applied ensemble machine learning to predict pitch accents. With decision tree as the baseline algorithm, two popular ensemble learning methods, bagging and boosting, were evaluated across different experiment conditions: using acoustic features only, using text-based features only; using both acoustic and text-based features. F0 related acoustic features are derived from unde...