Decision tree study is a predictive modelling tool that used over many grounds. It constructed through an algorithmic technique divided the dataset in different methods created on varied conditions. Decisions trees are extreme dominant algorithms drop under set of supervised algorithms. However, Trees appearance modest and natural, there nothing identical near how algorithm drives nearby proced...

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...

The main objective of a financial distress prediction model is to generate early warning signals.In this paper, we compare five bankruptcy prediction models, logit ,ANN ,CART, C5.0 and GP decision tree. Our empirical results reveal that the GP decision tree can outperform all the classifiers either in overall percentage of correct or k-fold cross validation test in out-sample. That is to say, G...

In this paper, we apply the CART ,C5.0 , GP decision tree classifiers and compares with logic model and ANN model for Taiwan listed electronic companies bankruptcy prediction. Results reveal that the GP decision tree can outperform all the classifiers either in overall percentage of correct or k-fold cross validation test in out sample. That is to say, GP decision tree model have the highest ac...

Historically, acute kidney injury (AKI) carried a deadly prognosis in the burn population. The aim of this study is to provide a modern description of AKI in the burn population and to develop a prediction tool for identifying patients at risk for late AKI. A large multi-institutional database, the Glue Grant's Trauma-Related Database, was used to characterize AKI in a cohort of critically ill ...

In this paper, an m-level optimal subtree based phonetic decision tree clustering algorithm is described. Unlike prior approaches, the m-level optimal subtree in the proposed approach is to generate log likelihood estimates using multiple mixture Gaussians for phonetic decision tree based state tying. It provides a more accurate model of the log likelihood variations in node splitting and it is...

Lazy learning algorithms, exemplified by nearestneighbor algorithms, do not induce a concise hypothesis from a given training set; the inductive process is delayed until a test instance is given. Algorithms for constructing decision trees, such as C4.5, ID3, and CART create a single “best” decision tree during the training phase, and this tree is then used to classify test instances. The tests ...

forest types mapping, is one of the most necessary elements in the forest management and silviculture treatments. traditional methods such as field surveys are almost time-consuming and cost-intensive. improvements in remote sensing data sources and classification –estimation methods are preparing new opportunities for obtaining more accurate forest biophysical attributes maps. this research co...

Article history: Received 13 March 2013 Received in revised form 28 August 2013 Accepted 30 December 2013 Available online 11 January 2014