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.

Analysis of the Characteristics of the Older Adults with Depression Using Data Mining Decision Tree Analysis

Abstract: Purpose: The purpose of this study was to develop a prediction model for the characteristics of older adults with depression using the decision tree method. Methods: A large dataset from the 2008 Korean Elderly Survey was used and data of 14,970 elderly people were analyzed. Target variable was depression and 53 input variables were general characteristics, family & social relationship, economic status, health status, health behavior, functional status, leisure & social activity, quality of life, and living environment. Data were analyzed by decision tree analysis, a data mining technique using SPSS Window 19.0 and Clementine 12.0 programs. Results: The decision trees were classified into five different rules to define the characteristics of older adults with depression. Classification & Regression Tree (C&RT) showed the best prediction with an accuracy of 80.81% among data mining models. Factors in the rules were life satisfaction, nutritional status, daily activity difficulty due to pain, functional limitation for basic or instrumental daily activities, number of chronic diseases and daily activity difficulty due to disease. Conclusion: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to these individual characteristics.

A New Method of Chinese Address Extraction Based on Address Tree Model

Abstract: Address is a spatial location encoding method of individual geographical area.In China,address planning is relatively backward due to the rapid development of the city,resulting in the presence of large number of non-standard address.The space constrain relationship of standard address model is analyzed in this paper and a new method of standard address extraction based on the tree model is proposed,which regards topological relationship as consistent criteria of space constraints.With this method,standard address can be extracted and errors can be excluded from non-standard address.Results indicate that higher math rate can be obtained with this method.

Decision model for selection of modernization measures: public building case

Abstract: The aim of our study is to present the decision model for selection of optimal combinations of modernization measures. the presented algorithm of decision synthesis method comprises method for integrated significance determination of efficiency indicators and multiple criteria decision methods. the paper also presents the case study illustrating the application of proposed model. as the alternative modernization measures can generate many alternative combinations the decision tree model was proposed as an efficient tool facilitating the analysis of big data and included in algorithm. three multiple criteria decision support methods based on quantitative measurements included in algorithm used to increase the reliability of the decision. the proposed algorithm is very suitable for evaluation of modernization decisions of the building and enables decision-maker to select the best performing alternative in terms of energy consumption, cost of instalment and other relevant criteria.

A tree-based classification model for analysis of a military software system

Abstract: Tactical military software is required to have high reliability. Each software function is often considered mission critical, and the lives of military personnel often depend on mission success. The paper presents a tree based modeling method for identifying fault prone software modules, which has been applied to a subsystem of the Joint Surveillance Target Attack Radar System, JSTPARS, a large tactical military system. We developed a decision tree model using software product metrics from one iteration of a spiral life cycle to predict whether or not each module in the next iteration would be considered fault prone. Model results could be used to identify those modules that would probably benefit from extra reviews and testing and thus reduce the risk of discovering faults later on. Identifying fault prone modules early in the development can lead to better reliability. High reliability of each iteration translates into a highly reliable final product. A decision tree also facilitates interpretation of software product metrics to characterize the fault prone class. The decision tree was constructed using the TREED-ISC algorithm which is a refinement of the CHAID algorithm. This algorithm partitions the ranges of independent variables based on chi squared tests with the dependent variable. In contrast to algorithms used by previous tree based studies of software metric data, there is no restriction to binary trees, and statistically significant relationships with the dependent variable are the basis for branching.