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.

Car allocation decisions in car-deficient households : the case of non-work tours

Abstract: This study examines the household interactions in the context of the car allocation choice decision in car-deficient households as part of an activity-scheduling process, focusing on non-work tours. A chi-square automatic interaction detector-based algorithm is applied to derive a decision tree using a large activity diary dataset recently collected in the Netherlands. The results show a satisfactory improvement in the goodness-of-fit of the decision tree model compared to the null model. Gender still plays a role. A descriptive analysis indicates that men, more often than women, get the car for non-work tours for which a car allocation decision needs to be made. Tour-level attributes also influence the household car allocation decision for non-work tours. Overall, men exert more influence on the car allocation decision for non-work tours, as indicated by the number of influential variables that relate to males. The developed models will be incorporated in a refinement of the ALBATROSS model - an existing computational process model of activity-travel choice.

Greedy learning of latent tree models for multidimensional clustering

Abstract: Real-world data are often multifaceted and can be meaningfully clustered in more than one way. There is a growing interest in obtaining multiple partitions of data. In previous work we learnt from data a latent tree model (LTM) that contains multiple latent variables (Chen et al. 2012). Each latent variable represents a soft partition of data and hence multiple partitions result in. The LTM approach can, through model selection, automatically determine how many partitions there should be, what attributes define each partition, and how many clusters there should be for each partition. It has been shown to yield rich and meaningful clustering results. Our previous algorithm EAST for learning LTMs is only efficient enough to handle data sets with dozens of attributes. This paper proposes an algorithm called BI that can deal with data sets with hundreds of attributes. We empirically compare BI with EAST and other more efficient LTM learning algorithms, and show that BI outperforms its competitors on data sets with hundreds of attributes. In terms of clustering results, BI compares favorably with alternative methods that are not based on LTMs.

Diagnosis of Breast Cancer using Decision Tree Models and SVM

Abstract: Breast cancer represents the second important cause of cancer deaths in women today and it is the most common type of cancer in women. Disease diagnosis is one of the applications where data mining tools are proving successful results. Data mining with decision trees is popular and effective data mining classification approach. Decision trees have the ability to generate understandable classification rules, which are very efficient tool for transfer knowledge to physicians and medical specialists. In fundamental truth, they provide trails to find rules that could be evaluated for separating the input samples into one of several groups without having to state the functional relationship directly. The objective of this paper is to examine the performance of recent invented decision tree modeling algorithms and compared with one that achieved by radial basis function kernel support vector machine (RBFSVM) on the diagnosis of breast cancer using cytological proven tumor dataset. Four models have been evaluated in decision tree: Chi-squared Automatic Interaction Detection (CHAID), Classification and Regression tree (CR classification accuracy, sensitivity, and specificity.

Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models

Abstract: In recent years, artificial neural network is advocated in modeling complex multivariable relationships due to its ability of fault tolerance; while decision tree of data mining technique was recommended because of its richness of classification arithmetic rules and appeal of visibility. The aim of our research was to compare the performance of ANN and decision tree models in predicting hospital charges on gastric cancer patients. Data about hospital charges on 1008 gastric cancer patients and related demographic information were collected from the First Affiliated Hospital of Anhui Medical University from 2005 to 2007 and preprocessed firstly to select pertinent input variables. Then artificial neural network (ANN) and decision tree models, using same hospital charge output variable and same input variables, were applied to compare the predictive abilities in terms of mean absolute errors and linear correlation coefficients for the training and test datasets. The transfer function in ANN model was sigmoid with 1 hidden layer and three hidden nodes. After preprocess of the data, 12 variables were selected and used as input variables in two types of models. For both the training dataset and the test dataset, mean absolute errors of ANN model were lower than those of decision tree model (1819.197 vs. 2782.423, 1162.279 vs. 3424.608) and linear correlation coefficients of the former model were higher than those of the latter (0.955 vs. 0.866, 0.987 vs. 0.806). The predictive ability and adaptive capacity of ANN model were better than those of decision tree model. ANN model performed better in predicting hospital charges of gastric cancer patients of China than did decision tree model.