1. Figure 5.3 is a decision tree for a hypothetical development project to develop and market a new product.

2. Another example is non-numerical decision tree analysis.

3. Firstly, this paper introduced decision tree algorithm theory.

4. A decision tree classifier is used to deal with the first task, which can find the load pattern preliminarily, and reduces the number of parameters to be adjusted.

5. Decision tree,() neural networks and Bayesian networks are the main tools of KDD.

6. The thesis researches an algorithm based on decision tree classifier for packet filtering.

7. Secondly, the paper analyzes the limitation of traditional decision tree method existing in credit risks appraisal, proposed a combined optimization and a multiple analysis decision tree algorithms.

8. Results show that the decision tree classifier can achieve higher classification accuracy.

9. Integrating the decision tree method, the classification of multi - classes was realized.

10. A decision tree was established for selecting the optimal inspection and repair strategy.

11. A method for constructing multiway decision tree based on quantized feature values (such as interval values, discrete values and symbolic values) is proposed.

12. A decision tree for substances in Articles is shown in Figure 1. No further action or obligation exists at this stage, although in the future the ECA may require a full registration.

13. Results The decision tree analysis revealed that the predicted costs of 3 cephalosporinses on the treatment of neonatal septicemia were RMB 2490,2586,2270 yuans respectively.

14. Methods:A decision tree was constructed to evaluate health effects of the program, such as averted sequelae of chlamydial infection.

15. Besides, a decision tree classifier(CART) is used to eliminate the unnecessary variables, and reduces the number of parameters to be adjusted.

16. The rank learning algorithm based on the decision tree is able to process categorical data and select relative features.

17. Evolutionary decision tree method has the advantage of global search.

18. With software program , we Simulate the Decision Tree Algorithm in the market segmentation.

19. A decision tree classifier was applied and a scalar product protocol was added, so that the need of privacy preserving is satisfied as well as the advantage of decision tree is retained.

20. We adopts decision tree based on inductive inference methods in selection of model structure.

21. In this paper, an enhanced ID3 decision tree algorithm called ES- ID3 with self-training - and - learning ability is proposed.

22. Improved CART algorithm of decision tree is put forward to solve at the problem of structure identification of ANFIS.

23. Traditionally, we solve a general Bayesian decision problem by using the decision tree analysis method.

24. The choice of attribute selection metric to split has an important impact on the shape and the depth of the resulting decision tree.

25. To meet the requirements of customer credit analysis, sales data from a certain steel mill are analyzed with the help of decision tree.

26. Decision tree classifier is an important data mining problem. The key issue in constructing the decision tree on data streams is to derive the best criterion of internal nodes.

27. According to the relationship between auto correlation function and its spectral density, a new type of decision tree method based on signal analysis theory is proposed in this paper.

28. The paper briefly introduced the concept of privacy preserving data mining technology and studied the application of decision tree classifier in this particular field.

29. Fourthly . making use of Learning from examples based on information theory, machine learning algorithm and machine learning decision tree is realized.

30. Firstly, rough sets is used to reduce condition attribute and remove redundancy attribute, and then, C4.5 is used to build decision tree, rule knowledge is extracted by decision tree pruning.

31. To research on market segmentation and decision tree of knowledge, the papers realize the basic algorithm.

32. "It's more of a Christmas tree approach where you constantly have to go back to the top to work yourself through the maze of the decision tree," Castonguay said.

33. This D-S decision tree is a new classification method applied to uncertain data and shows good performance and can efficiently avoid combinatorial explosion.

34. This article focuses on the pruning strategy of the decision tree pruning.

35. The principle and the decision steps of decision tree analysis method are introduced; at the same time the application of this method in construction bidding is illustrated.

36. But most of the existent decision tree algorithms suffer a problem, namely multivalue bios, in the process of attribute selection.

37. The results shows that the Bayesian Belief Network has better performance than the Decision Tree and Support Vector Machine.

38. In this paper, we have analyzed the characteristics of Chinese foreign exchange money laundering activities, and combined the decision tree approach with financial domain knowledge.

39. But, fuzzy decision tree induction is an important way for learning from examples with fuzzy representation.

40. Two methods for text categorization fuzzy rule extraction are presented based on fuzzy decision tree.

41. Experiment is the traditional method for analysing multivalue bios of decision tree algorithm, but it has a fault that we must have the expertise of the specific field.

42. Scalar Product Protocol is the keystone in building privacy-preserving decision tree.

43. The decision tree constructed with the new standard of attribute selection has the following characteristics:fewer leaf nodes, fewer levels of average depth, better generalization of leaf nodes.

44. Multivalue bios may result in inducing wrong knowledge from data set, and consequently result in the decline of the performance of decision tree.

45. Decision tree is a basic learning method in machine learning and data mining.

46. An MSS spectral form correlation model and a histogram decision tree classifier are presented.

47. This inference machine is based on decision tree, because of rules saving characteristic.

48. The comparable and analyzable experiment shows that this algorithm can make a minimize decision tree whose rules are true.

49. As a perfect quantitative analysis method the decision tree analysis method is practicable and simple and realize scientific decision in bid invitation.

50. In this paper, we implement an improved method based on decision tree on a specific problem of simulated local-area network on which a network intrusion detector is built.

51. The calculation of the decision tree uses the C4.5 algorithm, which takes information gain ratio as attribute choice criterion.

52. A decision tree is a graphic model of a decision process.

53. Comparing with the decision tree built by the existing algorithm, the decision tree built by the refined algorithm has a lower weighted path length(WPL), and more closer to optimal decision tree.

54. The data based on the decision tree inductive classification are applied to the analysis of the power plant heat cost, combined with the characteristics of electric power industry.

55. Methods:The decision tree classifier is used as a tool and the rate of classification accuracy is used to measure the consistency.

56. C 4.5 decision tree classifier, using the C language. U.S. with reference.

57. Second, this paper proposes a new decision tree algorithm, AF algorithm, which avoids multivalue bios.

58. Finally by comparison with C4.5 system, it is further confirmed that gradual increasing of the parameter within this interval may have the same effect as the crisp decision tree pruning.

59. The second method uses Bayes classifier in the first step and decision tree classifier in the second step.

60. Therefore, the decision tree method is a simple and useful tool for computer-aided diagnosis.

61. Decision tree is a tree - shaped diagram used to indicate the processing logic as a tool.

62. Then the decision tree and class association rules mining are used on the video attribute database to extract a decision tree classification rule set and a class association rule set respectively.

63. On the basis of analysing the multivalue bios, this paper proposes a new decision tree algorithm, AF algorithm,(http:///decision tree.html) which avoids the multivalue bios problem.

64. This paper uses a new attribute selection metric to construct decision tree, called the gain-ratio criterion, replace the gain criterion.

65. It improves the accuracy of attribute selection, overcomes the impact of noise data effectively and strengthened generalization ability of the decision tree.

66. Introduces decision tree and points out its key techniques: the choice of testing feature and tree pruning.

67. Decision tree learning is one of the widely used and practical methods for inductive inference.

68. A hierarchical decomposed support vector machines binary decision tree is used for classification.

69. Multiway decision tree has important applications in pattern recognition, artificial intelligence and decision support systems.

70. The proposed pruning stragey reduced computational complexity of the decision tree.

71. In the course of researching, we accomplish a Decision Tree classifier.

72. Control decision was made time by searching the decision tree for the path of minimum cost.

73. The paper briefly introduces the concept of privacy preserving data mining technology and studies the application of decision tree classifier in this particular field.

74. Experiments result proves that decision tree classifier is a effective classify method.

75. When analyze financial factor, decision tree method on the basis of inductive reasoning means is adopted to analyze the infection of financial factor to loan risk classification.

76. In bidding stage qualitative and quantitative analysis shall be applied for risk analysis, including methods of expert scoring, decision tree, expected loss, hierarchy analysis, and fuzzy evaluation.