Decision tree model

About: Decision tree model is a research topic. Over the lifetime, 2256 publications have been published within this topic receiving 38142 citations.

Modeling and Testing Landslide Hazard Using Decision Tree

Abstract: This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.

Super-logarithmic Depth Lower Bounds via Direct Sum in Communication Complexity (Preliminary Version)

Abstract: Is it easier to solve two communication problems to- gether than separately? This question is related to the complexity of the composition of boolean functions. Based on this relationship, an approach to separating NC' from P is outlined. Furthermore, it is shown that the approach provides a new proof of the separation of monotone NC' from monotone P.

Maximum likelihood regression trees

Abstract: We propose a method of constructing regression trees within the framework of maximum likelihood. It inherits the backward fitting idea of classification and regression trees (CART) but has more rigorous justification. Simulation studies show that it provides more accurate tree model selection compared to CART. The analysis of a baseball dataset is given as an illustration.

Nondeterministic Quantum Query and Communication Complexities

Abstract: We study nondeterministic quantum algorithms for Boolean functions f. Such algorithms have positive acceptance probability on input x iff f(x)=1. In the setting of query complexity, we show that the nondeterministic quantum complexity of a Boolean function is equal to its "nondeterministic polynomial" degree. We also prove a quantum-vs.-classical gap of 1 vs. n for nondeterministic query complexity for a total function. In the setting of communication complexity, we show that the nondeterministic quantum complexity of a two-party function is equal to the logarithm of the rank of a nondeterministic version of the communication matrix. This implies that the quantum communication complexities of the equality and disjointness functions are n+1 if we do not allow any error probability. We also exhibit a total function in which the nondeterministic quantum communication complexity is exponentially smaller than its classical counterpart.

Modeling and animation of botanical trees for interactive virtual environments

Abstract: This paper proposes a new modeling and animation method of botanical tree for interactive virtual environment. Some studies of botanical tree modeling have been based on the Growth Model, which can construct a very natural tree structure. However, this model makes it difficult to predict the final form of tree from given parameters; that is, if an objective form of a tree is given and it is to be reconstructed into a three-dimensional model, we have to change the parameters to reflect the structure by a trial-and-error technique. Thus, we propose a new top-down approach in which a tree's form is defined by volume data that is made from a captured real image set, and the branch structure is realized by simple branching rules. The tree model is described as a set of connected branch segments, and leaf models that consist of leaves and twigs that are attached to the branch segments. To animate the botanical trees, dynamics simulation is performed on the branch segments in two phases. In the first phase, each segment is assumed to be a rigid stick with a fixed end on one side, and rotational movements from influence of external forces are calculated in each segment independently. And the forces propagated from the tip of a branch to the root are calculated from the restoration force and thickness of the branch. Finally, the rotational movements of segments are executed in order from the base segment, and the fixed end of each segment is moved to the free end of the segment to be connected so as to maintain the relative angles between the segments. The proposed model is applied to many kind of botanical trees, and the model can successfully animate tree movements caused by external forces such as winds and human interaction to the branches.