Patient-specific models are instance-based learning algorithms that take advantage of the particular features of the patient case at hand to predict an outcome. We introduce two patient-specific algorithms based on decision tree paradigm that use AUC as a metric to select an attribute. We apply the patient specific algorithms to predict outcomes in several datasets, including medical datasets. ...

Vehicle occupants comprise a considerable proportion of traffic crash victims in Iran. This paper has focused on vehicleoccupants’ injury severity and employed the Classification and Regression Tree (CART) technique in order toidentify the most important variables affecting the injury severity of these road users in crashes occurred on rural freewaysand multilane highways in I...

the effects of different climatic, soil, geometric, and management factors on soil organic carbon (soc) degradation and sequestration potential was evaluated in the semi-arid zone of mereg watershed, west of iran. two nonparametric methods, viz. classification and regression tree (cart) and feed forward back propagation artificial neural network (ann) were compared with parametric multivariate ...

We study impurity-based decision tree algorithms such as CART, C4.5, etc., so as to better understand their theoretical underpinnings. We consider such algorithms on special forms of functions and distributions. We deal with the uniform distribution and functions that can be described as a boolean linear threshold functions and a read-once DNF. We show that for boolean linear threshold function...

We propose a new decision tree algorithm, Class Confidence Proportion Decision Tree (CCPDT), which is robust and insensitive to class distribution and generates rules which are statistically significant. In order to make decision trees robust, we begin by expressing Information Gain, the metric used in C4.5, in terms of confidence of a rule. This allows us to immediately explain why Information...

BACKGROUND DRG-systems are used to allocate resources fairly to hospitals based on their performance. Statistically, this allocation is based on simple rules that can be modeled with regression trees. However, the resulting models often have to be adjusted manually to be medically reasonable and ethical. METHODS Despite the possibility of manual, performance degenerating adaptations of the or...

[1] The main problem encountered when applying remote sensing and geographic information systems techniques for wildfire risk assessment is the necessity to integrate different data sources. The methods applied so far are usually based on regression techniques or on coefficients relying on experts’ knowledge. Hence fire managers are seeking an unbiased statistical model able to highlight the mu...

We study impurity-based decision tree algorithms such as CART, C4.5, etc., so as to better understand their theoretical underpinnings. We consider such algorithms on special forms of functions and distributions. We deal with the uniform distribution and functions that can be described as a boolean linear threshold functions or a read-once DNF. We show that for boolean linear threshold functions...

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In order to save the time and cost of friction wear experiments, coating composition (different contents Al, Ti, Cu elements), ratio hardness elastic modulus (H3/E2), vacuum heat treatment (VHT) temperature, form were used as input variables, rates high-entropy alloy (HEA) coatings output variables. The dataset was entirely obtained by experiment. Four machine learning algorithms (classificatio...