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.

Algorithms For Phylogeny Reconstruction In a New Mathematical Model

Abstract: The evolutionary history of a set of species is represented by a tree called phylogenetic tree or phylogeny. Its structure depends on precise biological assumptions about the evolution of species. Problems related to phylogeny reconstruction (i.e., finding a tree representation of information regarding a set of items) are widely studied in computer science. Most of these problems have found to be NP-hard. Sometimes they can solved polynomially if appropriate restrictions on the structure of the tree are fixed. This paper summarizes the most recent problems and results in phylogeny reconstruction, and introduces an innovative tree model, called Phylogenetic Parsimonious Tree, which is justified by significant biological hypothesis. Using PPT two problems are studied: the existence and the reconstruction of a tree both when sequences of characters and partial order on interspecies distances are given. We rove complexity results that confirm the hardness of this class of problems.

Adaptive decision tree fall detection method and system

Abstract: The invention discloses an adaptive decision tree fall detection method and system, and belongs to the technical field of human body behavior recognition and judgment. The method comprises the following steps: 1, acquiring three-axis acceleration and three-axis angular velocity data of falling and non-falling actions of a human body, and performing screening; 2, calculating resultant accelerationand resultant angular acceleration, dividing a training set, substituting a test set and a verification set into a TSFRESH library to calculate features, and screening and deleting useless features; 3, selecting preliminary important features by using a random forest sieve; 4, establishing a decision tree model for training and verification, and testing a result; 5, continuing to obtain a new sample, repeating the steps 2 to 4, and updating the decision tree model. According to the invention, an accurate tumble judgment result can be obtained through a decision tree algorithm with a small calculation amount; after a certain number of samples are collected, the decision tree model is updated, so the judgment precision of the algorithm can be further improved.

Soil Organic Matter Mapping by Decision Tree Modeling

Abstract: Based on a case study of Longyou County, Zhejiang Province, the decision tree, a data mining method, was used to analyze the relationships between soil organic matter (SOM) and other environmental and satellite sensing spatial data.The decision tree associated SOM content with some extensive easily observable landscape attributes, such as landform,geology, land use, and remote sensing images, thus transforming the SOM-related information into a clear, quantitative,landscape factor-associated regular system. This system could be used to predict continuous SOM spatial distribution.By analyzing factors such as elevation, geological unit, soil type, land use, remotely sensed data, upslope contributing area, slope, aspect, planform curvature, and profile curvature, the decision tree could predict distribution of soil organic matter levels. Among these factors, elevation, land use, aspect, soil type, the first principle component of bitemporal Landsat TM, and upslope contributing area were considered the most important variables for predicting SOM. Results of the prediction between SOM content and landscape types sorted by the decision tree showed a close relationship with an accuracy of 81.1%.

A Research on Object-oriented Analysis of Automatic Change Information Extraction Based on Decision Tree

Abstract: In order to extract change information automatically from the different time remote sensing images and to ensure the efficiency,a new automatic extraction method which is combined with the object-oriented image analysis technology based on the decision tree is presented in this paper.This method uses the feature index and shape,spectral,texture of the image as a feature set to establish the decision tree model for automatic classification.Organize and analyze the synthesized attributes of image objects classified above,then use it as the decision rule to make classification the second time.We can bring about the automatic extraction of image change information by 'double classification' which is based on the object-oriented image analysis.This method offers a new thought for automatic extraction of change information from remote sensing images.

Large-Scale Empirical Tests of the Markov Tree Model

Abstract: The Markov Tree model is a discrete-time option pricing model that accounts for short-term memory of the underlying asset. In this work, we compare the empirical performance of the Markov Tree model against that of the Black-Scholes model and Heston’s stochastic volatility model. Leveraging a total of five years of individual equity and index option data, and using three new methods for fitting the Markov Tree model, we find that the Markov Tree model makes smaller out-of-sample hedging errors than competing models. This comparison includes versions of Markov Tree and Black-Scholes models in which volatilities are strike- and maturity-dependent. Visualizing the errors over time, we find that the Markov Tree model yields more accurate and less risky single instrument hedges than Heston’s stochastic volatility model. A statistical resampling method indicates that the Markov Tree model’s superior hedging performance is due to its robustness with respect to noise in option data.