the classification and regression trees (cart) possess the advantage of being able to handlelarge data sets and yield readily interpretable models. in spite to these advantages, they are alsorecognized as highly unstable classifiers with respect to minor perturbations in the training data.in the other words methods present high variance. fuzzy logic brings in an improvement in theseaspects due ...

Aims: This study aimed at predicting bankruptcy based on two data mining techniques, i.e. logistic regression and classification and regression tree (CART). Study design: This was an applied, descriptiveanalytical, cross-sectional study. Place and Duration of Study: This research was carried out in Iran. Annual financial statements of companies in Tehran stock market (Iran) during 1999-2010 wer...

This work proposes new features to improve the pathological voice quality classification performance. They are the means, the variances, and the perturbations of the higher-order statistics (HOS) such as the skewness and the kurtosis. The HOS-based features show meaningful differences among normal, grade 1, grade 2, and grade 3 voices classified in the GRBAS scale. The jitter, the shimmer, the ...

In India and across the globe, liver disease is a serious area of concern in medicine. Therefore, it becomes essential to use classification algorithms for assessing the disease in order to improve the efficiency of medical diagnosis which eventually leads to appropriate and timely treatment. The study accordingly implemented various classification algorithms including linear discriminant analy...

Non asymptotic risk bounds for Classification And Regression Trees (CART) classifiers are obtained in the binary supervised classification framework under a margin assumption on the joint distribution of the covariates and the labels. These risk bounds are derived conditionally on the construction of the maximal binary tree and allow to prove that the linear penalty used in the CART pruning alg...

Educators need to understand how students are identified as at risk for reading problems. This study found that the classification and regression tree (CART) model—a type of predictive modeling that presents results in an easy-to-interpret “tree” format—predicted poor performance on the reading comprehension subtest of the Stanford Achievement Test as accurately as the logistic regression model...

In this paper, we study the use of boosted weak classifiers selected with AdaBoost algorithm in object detection. Our work is motivated by the good performance of AdaBoost in selecting discriminative features and the effectiveness of Classification and Regression Tree (CART) compared with other classification methods. First, we study the cascaded structure of the boosted weak classifier detecto...