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.

Model guided algorithm for mining unordered embedded subtrees

Abstract: Large amount of online information is or can be represented using semi-structured documents, such as XML. The information contained in an XML document can be effectively represented using a rooted ordered labeled tree. This has made the frequent pattern mining problem recast as the frequent subtree mining problem, which is a pre-requisite for association rule mining form tree-structured documents. Driven by different application needs a number of algorithms have been developed for mining of different subtree types under different support definitions. In this paper we present an algorithm for mining unordered embedded subtrees. It is an extension of our general tree model guided (TMG) candidate generation framework and the proposed U3 algorithm considers all support definitions, namely, transaction-based, occurrence-match and hybrid support. A number of experiments are presented on synthetic and real world data sets. The results demonstrate the flexibility of our general TMG framework as well as its efficiency when compared to the existing state-of-the-art approach.

Three measures of decoder complexity

Abstract: Three measures of the complexity of error correcting decoders are considered, namely, logic complexity, computation time and computational work (the number of logical operations). Bounds on the complexity required with each measure to decode with probability of error Pe, at code rate R are given and the complexity of a number of ad hoc decoding procedures is examined.

The Algorithm Analyses and Design about the Subjective Test Online Basing on the DOM Tree

Abstract: The test system in this essay is a test system under the mode of B/S (Browser/Server). In the test system, the subjective item grading technology is always a problem that limits the computer scoring technology development. The subjective items generally require to answer the questions in a way of language description, Since different person has different way of thinking, different level of understanding and different way of describing, the answers cannot be unanimously the same. Here we'll use the XML DOM object to do some research on the subjective item scoring problem. Solving the type of subjective items that has no unanimously same answers, such as short-answer questions, discussion questions etc. There are two factors that will affect the subjective item scoring: knowledge point and the nearness level. The unidirectional nearness algorithm in the fuzzy mathematics only focus on the keyword matching, but ignore the scoring of the knowledge point and the nearness level of the whole question's answering. First we'll analyses the DOM tree's structure and the tree model, research the use of the navigation document tree, DOM tree object, attribute data's reading and DOM tree DFS(depth first search) traversal method. And then discuss and build a automated scroing system's workflow based on XML DOM tree. Design a scoring system algorithm that can solve the short-answer, discussion-question kind of subjective items problem.

A Post-Pruning Decision Tree Algorithm Based on Bayesian

Abstract: The C4.5 Algorithm can result in a thriving decision tree and will overfit the training data while training the model. In order to overcome those disadvantages, this paper proposed a post-pruning decision tree algorithm based on Bayesian theory, in which each branch of the decision tree generated by the C4.5 algorithm is validated by Bayesian theorem, and then those branches that do not meet the conditions will be removed from the decision tree, at last a simple decision tree will be generated. The proposed algorithm can be verified by the data provided by the Beijing key disciplines platform and the Beijing Master and Dr. Platform. The result shows that the algorithm can the most unreliable and uneven branches. And compared with the C4.5 algorithm, the proposed algorithm has a higher prediction accuracy and a broader coverage.

L-Tree: A Local-Area-Learning-Based Tree Induction Algorithm for Image Classification.

Abstract: The decision tree is one of the most effective tools for deriving meaningful outcomes from image data acquired from the visual sensors. Owing to its reliability, superior generalization abilities, and easy implementation, the tree model has been widely used in various applications. However, in image classification problems, conventional tree methods use only a few sparse attributes as the splitting criterion. Consequently, they suffer from several drawbacks in terms of performance and environmental sensitivity. To overcome these limitations, this paper introduces a new tree induction algorithm that classifies images on the basis of local area learning. To train our predictive model, we extract a random local area within the image and use it as a feature for classification. In addition, the self-organizing map, which is a clustering technique, is used for node learning. We also adopt a random sampled optimization technique to search for the optimal node. Finally, each trained node stores the weights that represent the training data and class probabilities. Thus, a recursively trained tree classifies the data hierarchically based on the local similarity at each node. The proposed tree is a type of predictive model that offers benefits in terms of image's semantic energy conservation compared with conventional tree methods. Consequently, it exhibits improved performance under various conditions, such as noise and illumination changes. Moreover, the proposed algorithm can improve the generalization ability owing to its randomness. In addition, it can be easily applied to ensemble techniques. To evaluate the performance of the proposed algorithm, we perform quantitative and qualitative comparisons with various tree-based methods using four image datasets. The results show that our algorithm not only involves a lower classification error than the conventional methods but also exhibits stable performance even under unfavorable conditions such as noise and illumination changes.