Classification and Regression Tree

Classification and regression trees are becoming increasingly popular for partitioning data and identifying local structure in small and large datasets. Classification trees include those models in which the dependent variable (the predicted variable) is categorical. Regression trees include those in which it is continuous. This paper discusses pitfalls in the use of these methods and high- lights where they are

SUMMARY In the present study we evaluate the performance of different Machine Learning Methods to predict the unreported data on Caretta caretta by-catch by the Uruguayan longline fishery in the Southwestern Atlantic Ocean. The methods evaluated were Classification And Regression Trees, Random Forest, CForest and Support Vector Machines, and was selected the model with minor predictive error rate. We used on board observed data to predict logbook unreported loggerhead by-catch during 1998 to 2007 using different explanatory variables. Random Forests and CForest were the method selected because its presents the minor predictive error rate. The Random Forest approach predicted a total capture of 13 065 and CForest 12 892 loggerhead turtles during the study period. We also evaluate the variable importance in the prediction for both methods. The year, type of fishing gear and month are the variables most important in the by-catch of loggerhead sea turtles. Machine Learning methods appea...

Agricultural and Forest Meteorology j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / a g r f o r m e t a b s t r a c t Leaf wetness (LW) is one of the most important input variables of disease simulation models because of its fundamental role in the development of the infection process of many fungal pathogens. The low reliability of LW sensors and/or their rare use in standard weather stations has led to an increasing demand for reliable models that are able to estimate LW from other meteorological variables. When working on large databases in which data are interpolated in grids starting from weather stations, LW estimation is often penalized by the lack of hourly inputs (e.g., air relative humidity and air temperature), leading researchers to generate such variables from the daily values of the available weather data. Although it is possible to find several papers about models for the estimation of LW, the behavior and reliability of these models we...

A methodology was developed to reconstruct the Roman topography and environmental features of the Sarno River plain, Italy, before the AD 79 eruption of the Somma–Vesuvius volcanic complex. We collected, localized and digitized more than 1800 core drilling data to gain a representative network of stratigraphical information covering the entire plain. Besides other stratigraphical data including the characteristics of the
pre-AD 79 stratum, the depth to the pre-AD 79 surface was identified from the available drilling documentations. Instead of a simple interpolation method, we used a machine based learning approach based on classification and regression trees to reconstruct the pre-AD 79 topography. We hypothesize that
the present-day topography reflects the ancient topography and related surface processes, because volcanic deposits from the AD 79 eruption coated the ancient landscape. Thus, ancient physiographic elements of the Sarno River plain are still recognizable in the present-day topography. Therefore, a high-resolution, present-day digital elevation model (DEM) was generated. A detailed terrain analysis yielded 15 different primary
and secondary topographic indices. Subsequently, a classification and regression model was applied to predict the depth of the pre-AD 79 surface combining present-day topographic indices with other physiographic data. This model was calibrated with the measured depth of the pre-AD 79 surface. The resulting pre-AD 79 DEM was compared with the classified characteristic of the pre-AD 79 stratum, identified from the drilling documentations. This allowed the reconstruction of pre-AD 79 environmental features of the Sarno River plain such as the ancient coastline, the paleo-course of the Sarno River and its floodplain.
To the knowledge of the authors, it is the first time that the pre-AD 79 topography of the Sarno River plain was systematically reconstructed using a detailed database and sophisticated data mining technologies.

Forest fires have long been annual events in many parts of Sumatra Indonesia during the dry season. Riau Province is one of the regions in Sumatra where forest fires seriously occur every year mostly because of human factors both on purposes and accidently. Forest fire models have been developed for certain area using the weightage and criterion of variables that involve the subjective and qualitative judging for variables. Determining the weights for each criterion is based on expert knowledge or the previous experienced of the developers that may result too subjective models. In addition, criteria evaluation and weighting method are most applied to evaluate the small problem containing few criteria. This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. The algorithm is applied on historic forest fires data for a district in Riau namely Rokan Hilir to develop a model for forest fires risk. The modeling forest fire risk includes variables related to physical as well as social and economic. The result is a spatial decision tree containing 138 leaves with distance to nearest river as the first test attribute.

Early prediction of liver disease is very important to save human life and take proper steps to control the disease. Decision Tree algorithms have been successfully applied in various fields especially in medical science. This research work explores the early prediction of liver disease using various decision tree techniques. The liver disease dataset which is select for this study is consisting of attributes like total bilirubin, direct bilirubin, age, gender, total proteins, albumin and globulin ratio. The main purpose of this work is to calculate the performance of various decision tree techniques and compare their performance. The decision tree techniques used in this study are J48, LMT, Random Forest, Random tree, REPTree, Decision Stump, and Hoeffding Tree. The analysis proves that Decision Stump provides the highest accuracy than other techniques.

This paper presents a fully automated scheme for breast den- sity estimation and asymmetry detection on mammographic images. Image preprocessing and segmentation techniques are first applied to the image, in order to extract the feature s for the breast density categorization. Also a new fractal- related feature is proposed for the classification. The clas - sification to 3 classes is

We review various recent approaches to making digital soil maps based on geographic information systems (GIS) data layers, note some commonalities and propose a generic framework for the future. We discuss the various methods that have been, or could be, used for fitting quantitative relationships between soil properties or classes and their 'environment'. These include generalised linear models, classification and regression trees, neural networks, fuzzy systems and geostatistics. We also review the data layers that have ...