BACKGROUND Dengue fever is a re-emerging viral disease commonly occurring in tropical and subtropical areas. The clinical features and abnormal laboratory test results of dengue infection are similar to those of other febrile illnesses; hence, its accurate and timely diagnosis for providing appropriate treatment is difficult. Delayed diagnosis may be associated with inappropriate treatment and ...

For classi cation problems with ordinal attributes very often the class attribute should increase with each or some of the explaining attributes. These are called classi cation problems with monotonicity constraints. Classical decision tree algorithms such as CART or C4.5 generally do not produce monotone trees, even if the dataset is completely monotone. This paper surveys the methods that hav...

ABSTRACT The ability to interpret machine learning models has become incredibly important as machine learning is increasingly used to inform consequential decisions. We propose an approach to interpreting complex, blackbox models by constructing global explanations that summarize their reasoning process. In our approach, a global explanation is a decision tree that approximates the blackbox mod...

A usual way of ASR accuracy evaluation is calculation of Word Error Rate (WER) and Sentence Error Rate (SER). The misrecognitions that contribute to WER are classified into three categories: deletions, insertions and substitutions. The paper presents a study about a contribution of intrinsic speech variabilities to the each of the error category. Decision tree (DT) analysis is used. Five DT sty...

BACKGROUND The objective of this study was to determine a diagnostic classification scheme using a decision tree based model. MATERIALS AND METHODS The study was conducted as a retrospective case-control study in Imam Khomeini hospital in Tehran during 2001 to 2009. Data, including demographic and clinical-pathological characteristics, were uniformly collected from 624 females, 312 of them we...

Decision trees are a popular technique in statistical data classification. They recursively partition the feature space into disjoint sub-regions until each sub-region becomes homogeneous with respect to a particular class. The basic Classification and Regression Tree (CART) algorithm partitions the feature space using axis parallel splits. When the true decision boundaries are not aligned with...

A personal credit evaluation algorithm is proposed by the design of a decision tree with boosting algorithm, and classification carried out. By comparison conventional it shown that acts to speed up processing time. The Classification Regression Tree (CART) showed 90.95% accuracy, slightly higher than without boosting, 90.31%. To avoid overfitting model on training set due unreasonable data div...

Groundwater potential mapping is an important prerequisite for evaluating the exploitation, utilization, and recharge of groundwater. The study uses BFT (best-first decision tree classifier), CART (classification regression tree), FT (functional trees), EBF (evidential belief function) benchmark models, RF-BFTree, RF-CART, RF-FT ensemble models to map groundwater Wuqi County, China. Firstly, se...

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...

This paper introduces a novel splitting criterion parametrized by a scalar ‘α’ to build a class-imbalance resistant ensemble of decision trees. The proposed splitting criterion generalizes information gain in C4.5, and its extended form encompasses Gini(CART) and DKM splitting criteria as well. Each decision tree in the ensemble is based on a different splitting criterion enforced by a distinct...