It is usually not difficult to visualize an entire classification tree in a single image without overwhelming the user with visual clutter. Interpretation becomes a problem when we are required to visualize thousands of trees at once. Random forests have become a very common data mining algorithm due to advantages of high accuracy and ability to handle large amounts of input variables. A random...

The motivation for this study was to learn to predict forest fires in Slovenia using different data mining techniques. We used predictive models based on data from a GIS (geographical information system), the weather prediction model Aladin and MODIS satellite data. We examined three different datasets: one only for the Kras region, one for whole Primorska region and one for continental Sloveni...

There are a number of learning methods that provide solutions to classification and regression problems, including Linear Regression, Decision Trees, KNN, and SVMs. These methods work well in many applications, but they are challenged for real world problems that are noisy, nonlinear or high dimensional. Furthermore, missing data (e.g., missing historical features of companies in stock data), i...

Various random combinatorial objects, such as mappings, trees, forests, and subsets of a finite set, are constructed with probability distributions related to the binomial and multinomial expansions due to Abel, Cayley and Hurwitz. Relations between these combinatorial objects, such as Joyal’s bijection between mappings and marked rooted trees, have interesting probabilistic interpretations, an...

Random forests are a popular classification method based on an ensemble of a single type of decision trees from subspaces of data. In the literature, there are many different types of decision tree algorithms, including C4.5, CART, and CHAID. Each type of decision tree algorithm may capture different information and structure. This paper proposes a hybrid weighted random forest algorithm, simul...