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.

Low state-space complexity and high coverage Markov browsing forecast

Abstract: Browsing the World Wide Web (WWW) involves traversing hyperlink connections among documents. The ability to forecast browsing patterns can solve many problems that face producers and consumers of WWW content. Although Markov models have been found well suited to forecasting browsing modes, they have some drawbacks. To solve them, we present a new model, Markov tree model (MTM), to forecast user-browsing modes. It aggregates user-browsing information by a tree. By this structure, a forecast model can't generate an explosive number of states. All the forecast process can be performed on the MTM. During the forecast procedure, a recursive process is adopted to handle the problem of low coverage. If a higher sequence can't get a result, a lower sequence may be used. Experiments confirm that MTM can get higher coverage and lower state complexity. It can be widely used in prefetching, link prediction and recommendation, etc.

Development of Micro Wireless Sensor Platforms for Collecting Data on Passenger-Freight Interactions

Abstract: In this report, the authors propose an in-node microprocessor-based vehicle classification approach to analyze and determine the types of vehicles passing over a 3-axis magnetometer sensor. Their approach for vehicle classification utilizes J48 classification algorithm implemented in Weka (a machine learning software suite). J48 is a Quinlan's C4.5 algorithm, an extension of decision tree machine learning based on ID3 algorithm. The decision tree model is generated from a set of features extracted from vehicles passing over the 3-axis sensor. The features are attributes provided with correct classifications to the J48 training algorithm to generate a decision tree model with varying degrees of classification rates based on cross-validation. Ideally, using fewer attributes to generate the model allows for the highest computational efficiency due to fewer features needed to be calculated while minimalizing the tree with fewer branches. The generated tree model can then be easily implemented using nested if-loops in any language on a multitude of microprocessors. In addition, setting an adaptive baseline to negate the effects of the background magnetic field allows reuse of the same tree model in multiple environments. The result of their experiment shows that the vehicle classification system is effective and efficient with the accuracy at nearly 100%.

Random Forest and Concept of Decision Tree Model

Abstract: The method of random forest (RF) is a generalized advanced decision trees methods or techniques in which the clinical data space recursively portioned (usually binary split) according to the values of one or more predictor variables, such that the observations within a portion becomes more and more homogeneous. Moreover, the RF techniques come with a built-in protection against the overfitting by using a part of the data sets that each tree in the forest has not been calculated by its goodness of fit. Thus, the RF is a more reliable method extract decision-making approach for clinical research (in vivo or in vitro). In this regard, this chapter describes various aspects of random forest on real applications towards clinical and life science research; the model has been demonstrated by real data sets, and we also explored practical application of random forest for solving the real problems of clinical research. We examined how basic decision trees work, how individual decisions trees are combined to make a random forest, and ultimately discover why random forests are so good at what they do. Many illustrations and eye-catching figures accord to describe the model building. Summing of the research findings, the Random forests is advanced tool for clinical research each individual tree to create an uncorrelated forest of trees whose prediction by committee is more accurate than that of any individual tree.

Intelligent air-conditioning temperature control method based on decision tree classification algorithm

Abstract: The invention relates to the air conditioning field and discloses an air conditioning intelligent temperature control method based on a decision tree classification algorithm, which is used for improving the automatic control ability of the air conditioning, improving the user experience and reducing the discomfort caused by overblowing the air conditioning. A large amount of data need to be collected in advance to generate a training set, and the threshold value of suitable condition is obtained by health system, and then a decision tree is built and refined by using the training set, Establishing Decision Tree Model, and then utilizing the generated decision tree to obtain the attribute values of the environmental data from the root node sequentially from the information acquisition device in the system work, Until reaching a leaf node, to find the record of the class, and then through the classification decision tree to judge the data, and under various environmental conditions to achieve the results of the optimal processing and make the final control operation. The invention is suitable for intelligent temperature control of air conditioning.